{"meta":{"query_hash":"e54e7336da30","filters":{"venue":"BMC Medical Imaging"},"cohort_total":80,"direct_labels_cover":1,"predictions_cover":80,"exported":80,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/e54e7336da30","api":"https://metacan.xera.ac/api/v1/cohort?venue=BMC+Medical+Imaging"},"results":[{"id":"W1488415954","doi":"10.1186/1471-2342-6-3","title":"Percutaneous subclavian artery stent-graft placement following failed ultrasound guided subclavian venous access","year":2006,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Central Venous Catheters and Hemodialysis","field":"Health Professions","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Royal University Hospital","funders":"","keywords":"Percutaneous; Subclavian artery; Medicine; Radiology; Subclavian vein; Venous access; Stent; Surgery","score_opus":0.0340800426387138,"score_gpt":0.3681736599722992,"score_spread":0.33409361733358545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488415954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9758596,0.0006312881,0.010248658,0.0018927288,0.0021015247,0.001166635,0.000027216653,0.00041790566,0.0076544527],"genre_scores_gemma":[0.9924279,0.00006971482,0.00089834555,0.0029824919,0.0013487057,0.000089824316,0.00025026363,0.0001006253,0.0018321527],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9939292,0.00065195706,0.0013540379,0.0008086337,0.0014649256,0.0017912218],"domain_scores_gemma":[0.9966647,0.0014206494,0.00032615525,0.00071173476,0.00012646614,0.00075031235],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013854549,0.0004613626,0.00082097255,0.00025078803,0.0014076317,0.00015923344,0.00082230114,0.00028943628,0.0029671805],"category_scores_gemma":[0.00059193117,0.0004105076,0.0005344114,0.00037794936,0.00015334642,0.0003276333,0.00027992163,0.0009850519,0.00047950385],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013194463,0.0009861329,0.9482743,0.00048639448,0.00018605913,0.0024232413,0.0033714385,0.00010051642,0.0040238923,0.00021337876,0.036262523,0.0035401327],"study_design_scores_gemma":[0.036070693,0.00087089225,0.7151219,0.010571961,0.004906925,0.005256687,0.045476373,0.04259568,0.0012539807,0.00803911,0.119178765,0.010657028],"about_ca_topic_score_codex":0.0024519344,"about_ca_topic_score_gemma":0.003172437,"teacher_disagreement_score":0.23315243,"about_ca_system_score_codex":0.0006508672,"about_ca_system_score_gemma":0.0009439297,"threshold_uncertainty_score":0.9998924},"labels":[],"label_agreement":null},{"id":"W1573616466","doi":"10.1186/1471-2342-5-2","title":"Molecular mapping of periodontal tissues using infrared microspectroscopy","year":2005,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics; University of Manitoba","funders":"","keywords":"Connective tissue; Pathology; Raman microspectroscopy; Infiltration (HVAC); Chemistry; Epithelium; Biomedical engineering; Materials science; Medicine; Raman spectroscopy","score_opus":0.012681562724063442,"score_gpt":0.33701407054216576,"score_spread":0.3243325078181023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573616466","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8558626,0.0027896648,0.13716456,0.00028920893,0.000064077896,0.00012450399,0.000006096998,0.000039718743,0.0036595496],"genre_scores_gemma":[0.9113237,0.00024157805,0.08698275,0.0006381126,0.00046087897,0.000013322642,0.000043577995,0.000033480737,0.00026256216],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99812883,0.000056858957,0.00036440982,0.00037020768,0.00063647854,0.0004432158],"domain_scores_gemma":[0.9992024,0.000032560132,0.00007598585,0.0003230043,0.000076609824,0.00028945762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035272277,0.00016768002,0.00022480312,0.000073894196,0.000056629022,0.000022341366,0.00044688585,0.00016816436,0.00039313294],"category_scores_gemma":[0.00064358895,0.00015424688,0.00012995911,0.000165118,0.000504463,0.000007056359,0.00035011768,0.00022922078,0.000006765591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031392028,0.000071896284,0.006183583,0.000052410192,0.00001624416,0.000011947623,0.000022651742,0.0000015750983,0.98894334,0.000087648354,0.0013802708,0.0031970346],"study_design_scores_gemma":[0.00039326822,0.000041123876,0.00020493897,0.00011659895,0.0000073929536,0.000058281985,0.0000524773,0.0027567206,0.9689675,0.00014614429,0.027081909,0.00017365883],"about_ca_topic_score_codex":0.000023597891,"about_ca_topic_score_gemma":0.0000047109884,"teacher_disagreement_score":0.055461127,"about_ca_system_score_codex":0.000049786966,"about_ca_system_score_gemma":0.00027641433,"threshold_uncertainty_score":0.62900054},"labels":[],"label_agreement":null},{"id":"W1637723822","doi":"10.1186/1471-2342-6-11","title":"Semi-automated segmentation and quantification of adipose tissue in calf and thigh by MRI: a preliminary study in patients with monogenic metabolic syndrome","year":2006,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Nuclear Structure and Function","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"Ontario Genomics; Ontario Genomics Institute; Genome Canada; Heart and Stroke Foundation of Canada","keywords":"Adipose tissue; Thigh; Magnetic resonance imaging; Computer science; Radiology; Segmentation; Medicine; Anatomy; Artificial intelligence; Internal medicine","score_opus":0.003413156015631392,"score_gpt":0.23103870284737238,"score_spread":0.227625546831741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1637723822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978719,0.0009713471,0.0008105283,0.000021689659,0.00002470079,0.00027711893,0.0000044715885,0.000009214533,0.000009060626],"genre_scores_gemma":[0.99952275,0.000024379384,0.00025775965,0.000022239663,0.000009860272,0.000011939682,0.00013309518,0.000008991206,0.000009004585],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99931943,0.000070139904,0.00018060145,0.00020323033,0.00013768663,0.00008891568],"domain_scores_gemma":[0.99978906,0.000012138058,0.000057173034,0.00008748685,0.00002071821,0.000033444394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012525551,0.000074870324,0.00011204454,0.00005350326,0.000017156488,0.000008403969,0.000034553083,0.00004789899,0.0000038559865],"category_scores_gemma":[0.000026129741,0.00006334157,0.000006257692,0.0000854057,0.000048593374,0.000009271494,0.000028670975,0.000050605486,2.858505e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014666496,0.00017369179,0.9642045,0.000027667531,0.0000104668125,0.0000038779426,0.00007158301,0.000024908919,0.0330044,0.0000030901672,0.00009221292,0.002236942],"study_design_scores_gemma":[0.0016307173,0.0002219138,0.98998165,0.000025610858,0.000024065248,0.000011427205,0.00013487512,0.0022100487,0.005645497,0.0000066641423,0.000033076252,0.00007446667],"about_ca_topic_score_codex":0.00028453078,"about_ca_topic_score_gemma":0.00016785307,"teacher_disagreement_score":0.027358903,"about_ca_system_score_codex":0.0000056138783,"about_ca_system_score_gemma":0.000021403212,"threshold_uncertainty_score":0.2582994},"labels":[],"label_agreement":null},{"id":"W1771067798","doi":"10.1186/s12880-015-0069-9","title":"Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models","year":2015,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":159,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Texas Southwestern Medical Center","keywords":"Prostate cancer; Magnetic resonance imaging; Computer science; Artificial intelligence; Feature selection; Feature (linguistics); Support vector machine; Pattern recognition (psychology); Medicine; Cancer; Radiology","score_opus":0.032407525089254235,"score_gpt":0.3184049279533927,"score_spread":0.28599740286413844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1771067798","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3134876,0.60300785,0.054667283,0.016376615,0.0032231617,0.0048292773,0.00015422274,0.0032466797,0.0010072936],"genre_scores_gemma":[0.98747414,0.0018795845,0.006363051,0.0029312435,0.00030861408,0.00045077744,0.00006363887,0.000084380896,0.00044457943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970722,0.0001308971,0.00040519485,0.0007030199,0.0010187465,0.00066989235],"domain_scores_gemma":[0.99786264,0.00018018813,0.00014794362,0.00040592256,0.00050370116,0.0008996323],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026055076,0.0003897826,0.00054745126,0.00026478985,0.00013061626,0.00007145279,0.0001589461,0.00012668651,0.00007549679],"category_scores_gemma":[0.0003568898,0.00030985795,0.00013407471,0.0008002504,0.0002234475,0.00028586097,0.00011735056,0.0005622924,0.000044761226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046891888,0.00054477475,0.22442034,0.0003669048,0.000055756555,0.0010864164,0.0007895579,0.0006407452,0.00080345635,0.00000982575,0.013536922,0.7572764],"study_design_scores_gemma":[0.007840059,0.000121626945,0.082903706,0.0010109263,0.00020201107,0.0004730248,0.000304652,0.89684486,0.0013053557,0.00011214467,0.00851858,0.00036305643],"about_ca_topic_score_codex":0.0007483681,"about_ca_topic_score_gemma":0.00016138307,"teacher_disagreement_score":0.8962041,"about_ca_system_score_codex":0.0006485457,"about_ca_system_score_gemma":0.00077261194,"threshold_uncertainty_score":0.9999353},"labels":[],"label_agreement":null},{"id":"W1911170342","doi":"10.1186/s12880-015-0081-0","title":"Monte Carlo-based noise compensation in coil intensity corrected endorectal MRI","year":2015,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Electromagnetic coil; Noise (video); Scanner; Imaging phantom; Computer science; Magnetic resonance imaging; Compensation (psychology); Intensity (physics); Monte Carlo method; Signal-to-noise ratio (imaging); Image quality; Contrast-to-noise ratio; Nuclear medicine; Artificial intelligence; Physics; Optics; Radiology; Medicine; Mathematics; Image (mathematics); Telecommunications; Statistics","score_opus":0.034818862630696785,"score_gpt":0.328867382517049,"score_spread":0.2940485198863522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1911170342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37967104,0.00017292559,0.6106044,0.0053777257,0.00024833382,0.0006835518,0.000006682822,0.0004944267,0.002740924],"genre_scores_gemma":[0.9592368,0.000011617199,0.038555846,0.0018521459,0.00012205393,0.0000658994,0.00004250674,0.000019535313,0.00009357964],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987079,0.00004587739,0.00029733762,0.00026039127,0.00045254084,0.0002359355],"domain_scores_gemma":[0.9989048,0.0001578814,0.000071217735,0.00024867782,0.0002198061,0.00039761848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037254763,0.00012591285,0.0002749867,0.00010897973,0.000035156012,0.000009897619,0.00009843483,0.00006767689,0.00005284578],"category_scores_gemma":[0.0011359539,0.00011245257,0.00005392131,0.0002682375,0.000146187,0.00006708858,0.000052267907,0.00034066063,0.000023007804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013197908,0.0014410798,0.85933036,0.00021080869,0.000017340688,0.00081236963,0.001270028,0.002825401,0.0059436033,0.0009271968,0.041239243,0.08466275],"study_design_scores_gemma":[0.004282361,0.00006945575,0.04845162,0.00051038584,0.000040754872,0.00022705899,0.000674867,0.9353858,0.0038450898,0.00024114308,0.006019899,0.0002515363],"about_ca_topic_score_codex":0.00041010164,"about_ca_topic_score_gemma":0.00025854178,"teacher_disagreement_score":0.93256044,"about_ca_system_score_codex":0.00017892686,"about_ca_system_score_gemma":0.00045964788,"threshold_uncertainty_score":0.4585683},"labels":[],"label_agreement":null},{"id":"W1970150526","doi":"10.1186/1471-2342-13-30","title":"Quantification of cervical spine muscle fat: a comparison between T1-weighted and multi-echo gradient echo imaging using a variable projection algorithm (VARPRO)","year":2013,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Center for Advancing Translational Sciences; National Institutes of Health; Northwestern University","keywords":"Medicine; Magnetic resonance imaging; Whiplash; Neck pain; Nuclear medicine; Gradient echo; Concordance; Cervical vertebrae; Cervical spine; Algorithm; Radiology; Anatomy; Mathematics; Pathology; Surgery","score_opus":0.0313438287860976,"score_gpt":0.33059983334762466,"score_spread":0.29925600456152707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970150526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38485336,0.00037649416,0.6128599,0.00052916893,0.00017913894,0.00096919975,0.0000031277973,0.00010081863,0.00012875386],"genre_scores_gemma":[0.78279495,0.000015181673,0.21664776,0.00010857624,0.00025224773,0.000050365947,0.000071749084,0.00003058339,0.000028594886],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972623,0.00026607793,0.0008435369,0.0005290109,0.00070827064,0.00039078397],"domain_scores_gemma":[0.9984284,0.0003106416,0.00025757344,0.00031739273,0.00030931077,0.00037666416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010584143,0.00023568534,0.0005540431,0.00029608357,0.00014930748,0.000048271944,0.00010653563,0.00012765684,0.00016718534],"category_scores_gemma":[0.0009266969,0.00019297682,0.00011274044,0.00050297123,0.00031817998,0.00028956734,0.000082874496,0.00040060584,0.000012006565],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026995267,0.0008596057,0.46296284,0.002884754,0.000070962844,0.000004090283,0.0013145412,0.000014388848,0.107853815,0.000115038085,0.00017212502,0.42372087],"study_design_scores_gemma":[0.0015473397,0.000051678584,0.3054649,0.00077767565,0.000092783324,0.0000148861045,0.00073197513,0.690008,0.00094757735,0.00013988423,0.00006680671,0.00015649087],"about_ca_topic_score_codex":0.0016314317,"about_ca_topic_score_gemma":0.000014453342,"teacher_disagreement_score":0.6899936,"about_ca_system_score_codex":0.00013105343,"about_ca_system_score_gemma":0.00018831866,"threshold_uncertainty_score":0.7869366},"labels":[],"label_agreement":null},{"id":"W1980245463","doi":"10.1186/1471-2342-13-43","title":"Automatic detection of anomalies in screening mammograms","year":2013,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Cancer Centre; Saskatchewan Hospital; Janeway Children's Health and Rehabilitation Centre; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Artificial intelligence; Mammography; Pattern recognition (psychology); Wavelet; Classifier (UML); Abnormality; Medical diagnosis; Population; Breast cancer; Medicine; Radiology; Cancer","score_opus":0.012051177395817186,"score_gpt":0.24991893857150435,"score_spread":0.23786776117568717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980245463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20354986,0.00015259883,0.79520476,0.00031597505,0.00027159377,0.00011289804,6.035763e-8,0.00014715065,0.00024507812],"genre_scores_gemma":[0.9199603,0.00000492767,0.079790324,0.00013159396,0.000054775028,0.000039542112,2.270528e-7,0.000006791877,0.000011490638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862266,0.00010109175,0.00032152008,0.00022866974,0.0004960768,0.00023000866],"domain_scores_gemma":[0.9992538,0.00023066724,0.000100818026,0.00026938424,0.000046111658,0.00009923572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004931452,0.00008588087,0.00014213477,0.00017728613,0.000040050985,0.00006466589,0.00042966317,0.000044433797,0.000108033826],"category_scores_gemma":[0.00032827604,0.00008065085,0.000041761537,0.00047274417,0.00008804801,0.00063746766,0.00016667678,0.00016092713,0.0000290269],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.1623364e-7,0.000014845548,0.024584262,0.000052502975,0.0000027758447,0.0000038339294,0.00015766533,0.000029390345,0.0014058937,0.00010032222,0.000033234774,0.97361445],"study_design_scores_gemma":[0.0002466139,0.000014222607,0.077774204,0.00016591582,0.0000021369076,0.00003335461,0.0000820559,0.91636354,0.004242238,0.00092677824,0.00006170416,0.00008722997],"about_ca_topic_score_codex":0.0007042449,"about_ca_topic_score_gemma":0.00020289992,"teacher_disagreement_score":0.97352725,"about_ca_system_score_codex":0.000052722546,"about_ca_system_score_gemma":0.00007778755,"threshold_uncertainty_score":0.3288846},"labels":[],"label_agreement":null},{"id":"W1980396000","doi":"10.1186/s12880-015-0052-5","title":"Homotopic non-local regularized reconstruction from sparse positron emission tomography measurements","year":2015,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministero dello Sviluppo Economico; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Economic Development and Innovation","keywords":"Positron emission tomography; Tomography; Detector; Iterative reconstruction; Positron emission; Computer science; Regularization (linguistics); Reconstruction algorithm; Algorithm; Inverse problem; Projection (relational algebra); Physics; Nuclear medicine; Medical physics; Artificial intelligence; Mathematics; Optics; Medicine; Mathematical analysis","score_opus":0.04983001677754275,"score_gpt":0.315997357996205,"score_spread":0.26616734121866226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980396000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35885322,0.0003988217,0.6232206,0.010409454,0.0006174355,0.0007191811,0.0000060938946,0.0005587317,0.0052164486],"genre_scores_gemma":[0.84993446,0.000037301896,0.1469251,0.001868592,0.000675126,0.000097929005,0.00012814131,0.00004054974,0.00029278235],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971713,0.00011263714,0.0005100139,0.00049566233,0.0013330509,0.0003773735],"domain_scores_gemma":[0.99770075,0.00009937426,0.00012417929,0.0005535147,0.00020119426,0.001321009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089735264,0.00021807142,0.00039611064,0.00015471128,0.000102675076,0.000037777572,0.00023460922,0.00016241462,0.00040270944],"category_scores_gemma":[0.0007550008,0.00018024918,0.0001562008,0.00031776904,0.00035998112,0.00013283202,0.000114392205,0.0004869802,0.00005290536],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003510202,0.00075868814,0.43604365,0.0001736747,0.00012644376,0.00015060788,0.00024957597,0.000001270708,0.069013126,0.0001221294,0.102795266,0.39021453],"study_design_scores_gemma":[0.043125592,0.00061936094,0.13114765,0.013550853,0.0020985415,0.0034367598,0.0020987925,0.51192653,0.18745452,0.02149146,0.08013115,0.0029188206],"about_ca_topic_score_codex":0.00036609522,"about_ca_topic_score_gemma":0.000010338268,"teacher_disagreement_score":0.5119252,"about_ca_system_score_codex":0.00014162129,"about_ca_system_score_gemma":0.00053427776,"threshold_uncertainty_score":0.7350348},"labels":[],"label_agreement":null},{"id":"W1988165181","doi":"10.1186/1471-2342-13-20","title":"Comparison of T2 and T2*-weighted MR molecular imaging of a mouse model of glioma","year":2013,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thunder Bay Regional Research Institute; National Research Council Canada; University of Ottawa; Alberta Innovates; University of Calgary","funders":"FP7 People: Marie-Curie Actions; Canadian Institutes of Health Research; Ludwig Institute for Cancer Research","keywords":"T2 weighted; Glioma; Computer science; Magnetic resonance imaging; Nuclear magnetic resonance; Nuclear medicine; Medicine; Radiology; Cancer research; Physics","score_opus":0.02452195531979558,"score_gpt":0.3566673738957727,"score_spread":0.3321454185759771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988165181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47384897,0.0004585384,0.52447957,0.00056403695,0.000004950483,0.00023070835,0.0000044803865,0.000034850735,0.00037387465],"genre_scores_gemma":[0.84466267,0.000033572076,0.15505682,0.00014588886,0.00001190704,0.000033071366,0.000008826082,0.000017540562,0.00002971],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869055,0.00002086947,0.0005243202,0.00019179414,0.00040106452,0.0001713972],"domain_scores_gemma":[0.9989973,0.00010233444,0.00021103345,0.0003065034,0.00019147512,0.0001913866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014638693,0.00011351972,0.0004323153,0.00011894956,0.000021177315,0.0000035773623,0.00011890745,0.000044177716,0.00006563111],"category_scores_gemma":[0.00018526657,0.00009667268,0.000068998466,0.00016068248,0.00033023732,0.00007782516,0.0001012341,0.00015118782,0.0000013188594],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002616321,0.0003474614,0.05053119,0.0004265122,0.000013616011,0.0000043336895,0.00021548508,0.000096138334,0.9264932,0.0029537168,0.0007800677,0.01811211],"study_design_scores_gemma":[0.00064136164,0.000019808816,0.00053654617,0.00026318044,0.00004765076,0.000016205971,0.00018925543,0.6224051,0.3742035,0.0015733837,0.000029353561,0.00007467661],"about_ca_topic_score_codex":0.00010981955,"about_ca_topic_score_gemma":0.0000017157695,"teacher_disagreement_score":0.62230897,"about_ca_system_score_codex":0.000016014113,"about_ca_system_score_gemma":0.000104328785,"threshold_uncertainty_score":0.39421976},"labels":[],"label_agreement":null},{"id":"W1991226052","doi":"10.1186/1471-2342-8-12","title":"Does applying the Canadian Cervical Spine rule reduce cervical spine radiography rates in alert patients with blunt trauma to the neck? A retrospective analysis","year":2008,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Spinal Fractures and Fixation Techniques","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Radiography; Cervical spine; Blunt trauma; Retrospective cohort study; Cervical vertebrae; Radiology; Surgery","score_opus":0.010049192870865569,"score_gpt":0.2745842920545594,"score_spread":0.26453509918369383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991226052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95800257,0.00029891887,0.007957071,0.030616667,0.00014767885,0.0017649847,0.000012819623,0.000120064455,0.001079242],"genre_scores_gemma":[0.9887094,0.00004955514,0.0035081422,0.006921289,0.00036566265,0.00030457965,0.00006548476,0.000029954996,0.00004593333],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972109,0.00012142373,0.00048416771,0.00050194666,0.0011345864,0.00054696447],"domain_scores_gemma":[0.99839544,0.00008587074,0.00011203263,0.000507812,0.00021629717,0.00068251917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005988411,0.00026451808,0.0005311694,0.000608856,0.00040534223,0.00005559775,0.00036588576,0.00011586378,0.00043071067],"category_scores_gemma":[0.0005790969,0.000112622176,0.0002303676,0.0023640927,0.00033637712,0.00008942758,0.000064806125,0.0007265433,0.000007653968],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009720328,0.00012572396,0.9909774,0.000023998768,0.00013750522,0.00009037285,0.00047452477,0.000015406946,0.000009758849,0.00009512354,0.0008280138,0.007124953],"study_design_scores_gemma":[0.0010340511,0.00011749972,0.99060047,0.00017376497,0.0002189534,0.00003436937,0.00022240814,0.0028228988,0.00017442697,0.00006739828,0.0043462785,0.00018750007],"about_ca_topic_score_codex":0.04313412,"about_ca_topic_score_gemma":0.12533653,"teacher_disagreement_score":0.082202405,"about_ca_system_score_codex":0.00028156966,"about_ca_system_score_gemma":0.0003753601,"threshold_uncertainty_score":0.96323776},"labels":[],"label_agreement":null},{"id":"W1998057873","doi":"10.1186/1471-2342-10-5","title":"Building generic anatomical models using virtual model cutting and iterative registration","year":2010,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Genome Alberta; Genome Canada","keywords":"Computer science; Image registration; Representation (politics); Image (mathematics); Artificial intelligence; Range (aeronautics); Computer vision; Segmentation; Population; Image processing; Statistical model; Image segmentation; Pattern recognition (psychology)","score_opus":0.0717423305670791,"score_gpt":0.3457590405096023,"score_spread":0.27401670994252325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998057873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4355204,0.000018327884,0.56374633,0.00014025338,0.000073757066,0.000062426596,0.0000021366088,0.000047405607,0.00038893893],"genre_scores_gemma":[0.569243,0.0000025616214,0.43039736,0.00017482939,0.00014466273,0.000004279228,0.00000206091,0.000012112331,0.000019123792],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850774,0.00008185305,0.0003896115,0.00033614424,0.0004167736,0.0002678552],"domain_scores_gemma":[0.998757,0.00061937724,0.00013685516,0.00019188543,0.000064928456,0.00022995802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008584904,0.00015511125,0.00021979855,0.000083285035,0.0002081777,0.00011006671,0.00014523801,0.00012769307,0.0000707986],"category_scores_gemma":[0.001981608,0.00012867813,0.000053065167,0.0001439283,0.00014541674,0.0003206666,0.0001322943,0.00047277284,0.0000010914637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010317522,0.00010741458,0.0017567236,0.00005081706,0.000015120401,0.000029936738,0.00022328619,0.0019411804,0.034557547,0.9502721,0.00043356817,0.010601984],"study_design_scores_gemma":[0.00027342926,0.000004702172,0.000041772193,0.000048883354,0.000020442736,0.000082311155,0.000069536436,0.9004594,0.00039491823,0.09845022,0.000013711539,0.00014065641],"about_ca_topic_score_codex":0.000017586148,"about_ca_topic_score_gemma":0.000007726078,"teacher_disagreement_score":0.89851826,"about_ca_system_score_codex":0.000026377376,"about_ca_system_score_gemma":0.00014594686,"threshold_uncertainty_score":0.5247342},"labels":[],"label_agreement":null},{"id":"W2002758187","doi":"10.1186/1471-2342-9-12","title":"Test-retest variability of high resolution positron emission tomography (PET) imaging of cortical serotonin (5HT2A) receptors in older, healthy adults","year":2009,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Neurotransmitter Receptor Influence on Behavior","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health; Baycrest Hospital; University of Toronto","funders":"National Institute on Aging; National Institutes of Health; National Institute of Mental Health; University of Toronto; Ontario Innovation Trust","keywords":"Intraclass correlation; Positron emission tomography; Binding potential; 5-HT receptor; Medicine; Serotonin; Nuclear medicine; Dementia; Psychology; Internal medicine; Receptor; Clinical psychology; Psychometrics","score_opus":0.013401887782316681,"score_gpt":0.2925749965000937,"score_spread":0.27917310871777706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002758187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99429953,0.000060827155,0.001913055,0.0021980528,0.00037577222,0.0008653047,0.000016509495,0.00013674368,0.00013419855],"genre_scores_gemma":[0.9958959,0.00003684427,0.0027662134,0.0011659872,0.000056723467,0.000036934336,0.00000645113,0.000030195839,0.000004751978],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9944719,0.00094132905,0.0013618864,0.0009814785,0.0014867034,0.00075666665],"domain_scores_gemma":[0.9965244,0.0018443979,0.00037587565,0.00061498745,0.00012114506,0.00051920785],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014868662,0.00033015848,0.00058188324,0.0004107169,0.000111872374,0.000030770574,0.00068673945,0.00009726617,0.00028723732],"category_scores_gemma":[0.0061389506,0.0003141116,0.00017765553,0.0012016385,0.00067501393,0.00045916255,0.000106036154,0.00085589255,0.0000058527885],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039862137,0.0009784886,0.70815676,0.00018855737,6.6939833e-7,0.000057615136,0.00017650645,0.0000063327675,0.27896595,0.00012924215,0.000078129706,0.010863106],"study_design_scores_gemma":[0.0033874384,0.0004003775,0.6382754,0.0018246906,0.000030371531,0.00014305343,0.000061663,0.011322554,0.34359542,0.00048656398,0.000036373312,0.00043613015],"about_ca_topic_score_codex":0.00027440747,"about_ca_topic_score_gemma":0.000012792628,"teacher_disagreement_score":0.06988142,"about_ca_system_score_codex":0.00013213832,"about_ca_system_score_gemma":0.0003402573,"threshold_uncertainty_score":0.9999311},"labels":[],"label_agreement":null},{"id":"W2004816994","doi":"10.1186/1471-2342-14-37","title":"Noise-compensated homotopic non-local regularized reconstruction for rapid retinal optical coherence tomography image acquisitions","year":2014,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministero dello Sviluppo Economico; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Economic Development and Innovation","keywords":"Optical coherence tomography; Computer science; Image quality; Artificial intelligence; Computer vision; Noise reduction; Noise (video); Detector; Iterative reconstruction; Image restoration; Image resolution; Signal-to-noise ratio (imaging); Optics; Image processing; Image (mathematics); Physics; Telecommunications","score_opus":0.00718804239426401,"score_gpt":0.23209114774079062,"score_spread":0.2249031053465266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004816994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047565077,0.00008361567,0.9399457,0.00052170374,0.00042470742,0.00073143834,0.000019926223,0.000768064,0.009939752],"genre_scores_gemma":[0.80544543,0.000015994174,0.19333555,0.00016484673,0.00034388638,0.00054408336,0.00007158707,0.000057625748,0.000021026486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997592,0.000089131514,0.0006191763,0.0005168611,0.0005165943,0.000666258],"domain_scores_gemma":[0.99774945,0.00086031045,0.00006762382,0.0005507671,0.00020453642,0.0005673316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006957344,0.000316813,0.00041223576,0.00025672998,0.00024198186,0.00012496096,0.000454428,0.00019688827,0.0006811833],"category_scores_gemma":[0.00046940672,0.00032410416,0.0002777362,0.00067938026,0.00085356634,0.00027007153,0.000061665545,0.00048370013,0.000086436965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023876484,0.00057858945,0.012385631,0.0015681213,0.00031069195,0.000029989858,0.00027468512,0.00074599835,0.14832729,0.036196772,0.007943613,0.79139984],"study_design_scores_gemma":[0.0025981236,0.00009124369,0.016128028,0.00042177542,0.0001569725,0.00018367184,0.0002055842,0.96247435,0.008655605,0.0067285276,0.0015756728,0.0007804424],"about_ca_topic_score_codex":0.000008400831,"about_ca_topic_score_gemma":0.000013597461,"teacher_disagreement_score":0.96172833,"about_ca_system_score_codex":0.000058359583,"about_ca_system_score_gemma":0.00009267349,"threshold_uncertainty_score":0.9999211},"labels":[],"label_agreement":null},{"id":"W2014364573","doi":"10.1186/1471-2342-13-1","title":"Multimodal image registration of the scoliotic torso for surgical planning","year":2013,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"","keywords":"Torso; Computer science; Surgical planning; Computer vision; Scoliosis; Artificial intelligence; Similarity (geometry); Image-guided surgery; Position (finance); Image registration; Representation (politics); Medicine; Anatomy; Radiology; Image (mathematics); Surgery","score_opus":0.02719483573863687,"score_gpt":0.3360640357919979,"score_spread":0.30886920005336105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014364573","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98766714,0.0005849875,0.0023184768,0.005622991,0.00027123146,0.0011047928,0.000002978705,0.00003945025,0.0023879611],"genre_scores_gemma":[0.99578905,0.000010654784,0.003193313,0.00041835834,0.0002714797,0.00016904292,0.000008496811,0.00001456155,0.00012506417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986945,0.000048364403,0.000331816,0.00019451864,0.00050811126,0.00022268482],"domain_scores_gemma":[0.99880636,0.0005473436,0.00010744161,0.00023628092,0.0001123391,0.0001902347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029464095,0.00010884342,0.00023564577,0.000044378874,0.00007312296,0.00002694592,0.000107049724,0.000054411223,0.0003184032],"category_scores_gemma":[0.0011334443,0.00006463114,0.00016596043,0.000110403205,0.0001588233,0.00009468168,0.00004129096,0.00009794225,0.000012481003],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008878427,0.0006240289,0.97132385,0.000378057,0.00006337048,0.000052557825,0.00021488829,0.000009179325,0.0009815621,0.00069947273,0.017121386,0.008442852],"study_design_scores_gemma":[0.009314489,0.00014232729,0.93031037,0.0023312927,0.00024040669,0.00025524135,0.0005023898,0.04661071,0.007893184,0.00032579858,0.0018721233,0.00020164516],"about_ca_topic_score_codex":0.00016150976,"about_ca_topic_score_gemma":0.000009855899,"teacher_disagreement_score":0.04660153,"about_ca_system_score_codex":0.00005075489,"about_ca_system_score_gemma":0.00022579018,"threshold_uncertainty_score":0.3486292},"labels":[],"label_agreement":null},{"id":"W2017596461","doi":"10.1186/1471-2342-7-3","title":"Quantitative and qualitative differences in subcutaneous adipose tissue stores across lipodystrophy types shown by magnetic resonance imaging","year":2007,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Nuclear Structure and Function","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"Genome Canada; Heart and Stroke Foundation of Canada","keywords":"Lipodystrophy; Adipose tissue; Medicine; Magnetic resonance imaging; Thigh; Subcutaneous adipose tissue; Subcutaneous fat; Abdominal fat; Abdomen; Soft tissue; Subcutaneous tissue; Anterior compartment of thigh; Anatomy; Internal medicine; Radiology; Pathology; Obesity","score_opus":0.010275432179799092,"score_gpt":0.31176041156187717,"score_spread":0.3014849793820781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017596461","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9658568,0.026212435,0.007117428,0.00017945905,0.00019002944,0.000101296435,0.000020377298,0.000015900061,0.00030628315],"genre_scores_gemma":[0.99683404,0.0002456049,0.0022546486,0.00029207842,0.00013110894,0.0000042266083,0.000029545528,0.000019005372,0.00018975823],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99868035,0.00012805164,0.00023521089,0.0003747761,0.00024048782,0.00034113784],"domain_scores_gemma":[0.99944454,0.00018120364,0.000057685447,0.0001315765,0.000047252935,0.00013771246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004799316,0.00015637114,0.0001717079,0.00004301313,0.00008374464,0.000038617276,0.00012816246,0.00007806112,0.00004758042],"category_scores_gemma":[0.0005009235,0.00013671971,0.000026598327,0.00010146026,0.0003625635,0.0000107815595,0.00008656506,0.00016987497,0.0000050782646],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013755087,0.00012087095,0.15715405,0.00011804725,0.000026152695,0.00036057827,0.0090357335,0.0000013894319,0.30025735,0.00055117544,0.00346458,0.52753454],"study_design_scores_gemma":[0.010986371,0.0047910875,0.6210291,0.001369485,0.00019407409,0.0022165645,0.089249074,0.011030585,0.08734671,0.0032522776,0.16410501,0.004429657],"about_ca_topic_score_codex":0.00021486712,"about_ca_topic_score_gemma":0.0006830171,"teacher_disagreement_score":0.5231049,"about_ca_system_score_codex":0.000015338333,"about_ca_system_score_gemma":0.000044949687,"threshold_uncertainty_score":0.55752677},"labels":[],"label_agreement":null},{"id":"W2035132278","doi":"10.1186/1471-2342-13-26","title":"Correlated diffusion imaging","year":2013,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Diffusion MRI; Magnetic resonance imaging; Effective diffusion coefficient; Prostate cancer; Receiver operating characteristic; Medicine; Cancer; Diffusion imaging; Prostate; Medical imaging; Diffusion; Radiology; Computer science; Physics; Internal medicine","score_opus":0.011935086154348887,"score_gpt":0.28815955303615226,"score_spread":0.27622446688180335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035132278","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7807529,0.005309503,0.051748514,0.09827881,0.0035096053,0.0017502418,0.000004229973,0.0012839473,0.05736225],"genre_scores_gemma":[0.9813468,0.00047556887,0.0042465893,0.011824749,0.00071002607,0.00013608971,0.00002989024,0.00006662509,0.0011636515],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99769205,0.000070129616,0.0004029229,0.0003995232,0.00094311946,0.00049226277],"domain_scores_gemma":[0.9981589,0.0004972431,0.000082148035,0.00041171102,0.00014087372,0.0007091736],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00033841238,0.00019548465,0.00031681158,0.00013982713,0.00009383184,0.000051884726,0.00019318247,0.00007548726,0.016400404],"category_scores_gemma":[0.0010004817,0.00015975496,0.00011752282,0.00025867662,0.00020649882,0.00020541805,0.00016218725,0.00046887257,0.0015581605],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013659077,0.00012187214,0.7355093,0.0001099887,0.00001287653,0.00014058105,0.00010061666,9.785196e-7,0.001285767,0.000051876697,0.1627976,0.09985491],"study_design_scores_gemma":[0.0074320715,0.000048519018,0.58585316,0.0033896314,0.00025229168,0.0013351062,0.00078320014,0.33340144,0.0017187281,0.000742652,0.0643058,0.0007373916],"about_ca_topic_score_codex":0.0005912894,"about_ca_topic_score_gemma":0.000014762373,"teacher_disagreement_score":0.33340046,"about_ca_system_score_codex":0.00013985303,"about_ca_system_score_gemma":0.0003241246,"threshold_uncertainty_score":0.99921924},"labels":[],"label_agreement":null},{"id":"W2048183254","doi":"10.1186/1471-2342-12-26","title":"Quantitative evaluation of an automatic segmentation method for 3D reconstruction of intervertebral scoliotic disks from MR images","year":2012,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Intervertebral disk; Segmentation; Computer science; Sørensen–Dice coefficient; Artificial intelligence; Computer vision; Surgical planning; Medicine; Image segmentation; Radiology; Lumbar","score_opus":0.04660533727474855,"score_gpt":0.3805290662316652,"score_spread":0.3339237289569167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048183254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37283754,0.0005081534,0.62601185,0.000028035403,0.00032499328,0.00013837556,0.000015080466,0.000055322274,0.00008068246],"genre_scores_gemma":[0.629228,0.0000075699927,0.37055713,0.000015402113,0.0000919226,0.000026954938,0.000056676647,0.000014340015,0.0000020361892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980272,0.00036851465,0.00055247283,0.00014926288,0.0006944407,0.00020808716],"domain_scores_gemma":[0.9987203,0.0006266886,0.00015366694,0.00015699894,0.00015896183,0.00018337896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025595508,0.000123522,0.00032113126,0.00016515586,0.000030836112,0.000015083945,0.00011738687,0.00004996906,0.0004824802],"category_scores_gemma":[0.0019424125,0.000111133275,0.00010778572,0.00018373923,0.00011120254,0.00051467086,0.00001855954,0.000112785194,0.0000038661997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007965599,0.000108199005,0.015278282,0.0005976326,0.00017263628,3.0681406e-7,0.0012294461,0.0018104549,0.022159554,0.00006238744,0.00015725296,0.95841587],"study_design_scores_gemma":[0.00074600196,0.000014398579,0.005151867,0.00045005322,0.00043580137,0.000004968517,0.0013549925,0.9687845,0.022449192,0.0005008126,0.000001602513,0.00010581121],"about_ca_topic_score_codex":0.00016053655,"about_ca_topic_score_gemma":0.000018180195,"teacher_disagreement_score":0.966974,"about_ca_system_score_codex":0.000066502325,"about_ca_system_score_gemma":0.000073847936,"threshold_uncertainty_score":0.528282},"labels":[],"label_agreement":null},{"id":"W2062205667","doi":"10.1186/1471-2342-8-8","title":"Application of reinforcement learning for segmentation of transrectal ultrasound images","year":2008,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Reinforcement learning; Modality (human–computer interaction); Image segmentation; Computer vision; Ultrasound; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.01575274532697471,"score_gpt":0.298222903476396,"score_spread":0.28247015814942134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062205667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035398186,0.00008771428,0.9953449,0.0001255282,0.00006867535,0.00041843025,0.0000010787752,0.00012957177,0.00028429666],"genre_scores_gemma":[0.6478542,0.00007108981,0.3517426,0.0001280812,0.000034632914,0.0000902199,0.000024646333,0.000007993062,0.000046530957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981289,0.000085908076,0.0005657999,0.00024814453,0.00078673905,0.00018449714],"domain_scores_gemma":[0.998478,0.00074308267,0.00027191988,0.00021371343,0.00016592347,0.00012735756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006914399,0.00010431768,0.00020297781,0.000119679,0.000082210245,0.000012009584,0.00043784225,0.000041312072,0.000053804684],"category_scores_gemma":[0.0008472445,0.000098961034,0.00008934438,0.00023145955,0.0002375857,0.0003842843,0.000050338025,0.000113090355,0.0000024794579],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042454478,0.00021228421,0.021722566,0.000655073,0.000036345817,0.0000061303745,0.0026327448,0.0006998937,0.6206416,0.0018160595,0.0016658913,0.34986895],"study_design_scores_gemma":[0.00096013496,0.00008354625,0.0022420725,0.000089050285,0.000013249408,0.000046056855,0.00015658457,0.14244404,0.8534472,0.00031640285,0.000062341416,0.00013933086],"about_ca_topic_score_codex":0.000044079898,"about_ca_topic_score_gemma":0.0000015278986,"teacher_disagreement_score":0.6443144,"about_ca_system_score_codex":0.00003262834,"about_ca_system_score_gemma":0.00017918072,"threshold_uncertainty_score":0.40355137},"labels":[],"label_agreement":null},{"id":"W2069777119","doi":"10.1186/1471-2342-10-14","title":"Brainstem infarction in a patient with internal carotid dissection and persistent trigeminal artery: a case report","year":2010,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Intracranial Aneurysms: Treatment and Complications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Manitoba","keywords":"Trigeminal artery; Medicine; Brainstem; Dissection (medical); Carotid artery dissection; Anastomosis; Infarction; Radiology; Thrombosis; Internal carotid artery; Carotid arteries; Cardiology; Surgery; Internal medicine; Myocardial infarction","score_opus":0.009355612569344363,"score_gpt":0.25630906478089505,"score_spread":0.24695345221155068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069777119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99512297,0.000025407737,0.0014021068,0.0016566201,0.00017325403,0.00029776103,0.0000010605413,0.000039574854,0.0012812454],"genre_scores_gemma":[0.99866104,0.0000028695522,0.0007556999,0.00021259708,0.00015602639,0.000064996486,0.000017456807,0.000010007052,0.00011931966],"study_design_codex":"observational","study_design_gemma":"case_report","domain_scores_codex":[0.99904144,0.000027369593,0.00024964893,0.00024706466,0.0002750181,0.00015946857],"domain_scores_gemma":[0.9994474,0.00006269647,0.00006645369,0.00015377955,0.00006092359,0.00020876089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016306402,0.00010680629,0.00015006565,0.00010827564,0.00008135552,0.000024066074,0.000026468984,0.00004182842,0.000087766966],"category_scores_gemma":[0.00015370845,0.0000769863,0.00006030999,0.00010892791,0.00010434473,0.00007779042,0.000021589085,0.00032492564,0.0000037649993],"study_design_candidate":"case_report","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047461115,0.000611103,0.8305905,0.00012943771,0.000071640876,0.11712729,0.0017748497,0.0000013674373,0.0034202235,0.00018199843,0.00012760508,0.04548936],"study_design_scores_gemma":[0.0016739299,0.00025194607,0.08498388,0.00024553188,0.000111337395,0.9047579,0.0013797419,0.006058446,0.00014726682,0.000018264926,0.0002591097,0.00011261346],"about_ca_topic_score_codex":0.00039107376,"about_ca_topic_score_gemma":0.0015660872,"teacher_disagreement_score":0.7876306,"about_ca_system_score_codex":0.000051463154,"about_ca_system_score_gemma":0.00012647166,"threshold_uncertainty_score":0.31394103},"labels":[],"label_agreement":null},{"id":"W2151800036","doi":"10.1186/1471-2342-8-9","title":"Automatic volumetry on MR brain images can support diagnostic decision making","year":2008,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; Dunhill Medical Trust","keywords":"Dementia; Computer science; Artificial intelligence; Neuroimaging; Medicine; Disease; Pathology; Psychiatry","score_opus":0.019626086093280933,"score_gpt":0.3524213055758374,"score_spread":0.33279521948255647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151800036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9650638,0.00021300201,0.022157546,0.0045225243,0.00025139606,0.0004854278,0.0000060224907,0.00020019044,0.007100053],"genre_scores_gemma":[0.9867215,0.00007919257,0.003179991,0.008492769,0.00031220834,0.00004980387,0.000029580922,0.000039567833,0.0010953582],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961971,0.00016243437,0.0004336149,0.00044044002,0.0021095255,0.0006568659],"domain_scores_gemma":[0.9944443,0.0044304966,0.000070146576,0.0003328484,0.00012909224,0.0005931305],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010299833,0.00021408508,0.00035314113,0.0003286106,0.00021743978,0.000041757645,0.00021290201,0.00007001023,0.00758525],"category_scores_gemma":[0.019087845,0.00017395476,0.00014423794,0.00034540708,0.00032804708,0.00010470793,0.00016253488,0.00047294414,0.0005556041],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050112874,0.0002828048,0.67008513,0.00014478399,0.000023745026,0.0035592811,0.00009045228,2.7665314e-7,0.00014323679,0.000011852941,0.0565473,0.26906103],"study_design_scores_gemma":[0.0026775184,0.00027235516,0.9808711,0.0018296128,0.000066053966,0.00144627,0.00020241286,0.009700935,0.00066293357,0.00015351747,0.0018917326,0.00022556796],"about_ca_topic_score_codex":0.00003535766,"about_ca_topic_score_gemma":0.000016848742,"teacher_disagreement_score":0.31078598,"about_ca_system_score_codex":0.0001238598,"about_ca_system_score_gemma":0.0008106953,"threshold_uncertainty_score":0.99332196},"labels":[],"label_agreement":null},{"id":"W2157633021","doi":"10.1186/1471-2342-14-21","title":"Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI","year":2014,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Højteknologifonden; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Synarc; University of Southern California; Medpace; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Magnetic resonance imaging; Computer science; Alzheimer's disease; Disease; Neuroscience; Medicine; Artificial intelligence; Pathology; Psychology; Radiology","score_opus":0.044759930777630554,"score_gpt":0.3120548093794409,"score_spread":0.26729487860181034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157633021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04599642,0.00016066876,0.4921554,0.44406772,0.002614627,0.0022350857,0.00004677708,0.0006311505,0.012092141],"genre_scores_gemma":[0.94202584,0.0000013539053,0.0015338304,0.05543043,0.00039912976,0.00015656903,0.0000061848086,0.0000322451,0.00041440784],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972426,0.0005230724,0.00022395798,0.0007578595,0.00086346094,0.0003890119],"domain_scores_gemma":[0.96832174,0.03075931,0.00010527773,0.00035603173,0.0000686504,0.00038899135],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0007433544,0.00021614827,0.0002133961,0.00009058279,0.00041436352,0.000048549122,0.0002635657,0.00004686893,0.0001295553],"category_scores_gemma":[0.14857158,0.00017663508,0.00014908922,0.00016298931,0.00041993088,0.00030157916,0.000109279405,0.00027580536,0.000050034865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0065093953,0.00068837765,0.272748,0.0005831262,0.000121650475,0.00036904894,0.00065698335,0.0017654056,0.00122972,0.23295477,0.41123903,0.07113452],"study_design_scores_gemma":[0.00227881,0.00012170537,0.06062886,0.00021050649,0.000066674715,0.00002553351,0.000034925724,0.8746181,0.0009350899,0.035151586,0.025474828,0.00045338206],"about_ca_topic_score_codex":0.000011904549,"about_ca_topic_score_gemma":0.000012957385,"teacher_disagreement_score":0.8960294,"about_ca_system_score_codex":0.000067654626,"about_ca_system_score_gemma":0.00029175455,"threshold_uncertainty_score":0.8586004},"labels":[],"label_agreement":null},{"id":"W2160198553","doi":"10.1186/1471-2342-13-19","title":"A workstation-integrated peer review quality assurance program: pilot study","year":2013,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiology practices and education","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Calgary Laboratory Services; University of Alberta","funders":"","keywords":"Concordance; Medicine; Medical physics; Quality assurance; Radiological weapon; Peer review; Workstation; Excellence; Computer science; Radiology; Pathology","score_opus":0.10945916720844337,"score_gpt":0.4559122637643657,"score_spread":0.3464530965559224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160198553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77650386,0.012388497,0.0076095373,0.18745665,0.001398966,0.005672912,0.000001966979,0.0004998678,0.00846774],"genre_scores_gemma":[0.9748347,0.00093738927,0.009610788,0.008756606,0.0003880144,0.00081883196,0.000112530455,0.000024067202,0.0045171026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99755514,0.0004603885,0.0005064504,0.00030467167,0.0009036308,0.0002697349],"domain_scores_gemma":[0.9977455,0.00070280826,0.00017891233,0.00033568183,0.00070979283,0.00032726146],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027740332,0.00013396856,0.0003580955,0.00004461287,0.000078910045,0.000048274742,0.00012610127,0.00003226714,0.0022174902],"category_scores_gemma":[0.017319573,0.000095445794,0.000047115274,0.00029482978,0.00010466929,0.00026881503,0.00002211602,0.0004320525,0.00035446376],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005154095,0.0021118533,0.6191243,0.0007702797,0.00005250561,0.00001877379,0.00023755897,2.280836e-7,0.00004099143,0.00002625034,0.23514464,0.14242107],"study_design_scores_gemma":[0.0013967269,0.0003975993,0.9533519,0.0019607313,0.0001792902,0.00012640272,0.0018809167,0.0016033596,0.0000026941318,0.00005590334,0.038873628,0.00017084667],"about_ca_topic_score_codex":0.0010131978,"about_ca_topic_score_gemma":0.000104368126,"teacher_disagreement_score":0.3342276,"about_ca_system_score_codex":0.000056253903,"about_ca_system_score_gemma":0.0005740062,"threshold_uncertainty_score":0.9986946},"labels":[],"label_agreement":null},{"id":"W2281620867","doi":"10.1186/s12880-016-0112-5","title":"A sinogram denoising algorithm for low-dose computed tomography","year":2016,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TRIUMF; University of British Columbia Hospital","funders":"University of British Columbia","keywords":"Noise reduction; Noise (video); Computer science; Projection (relational algebra); Algorithm; Shot noise; Image quality; Artificial intelligence; Iterative reconstruction; Computer vision; Image (mathematics); Detector","score_opus":0.02140962268604206,"score_gpt":0.32845692789731606,"score_spread":0.307047305211274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281620867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009943703,0.0002856467,0.97204685,0.015765402,0.00018249005,0.0007758011,0.000015593478,0.00070303463,0.00028144804],"genre_scores_gemma":[0.2227191,0.00008180365,0.76874965,0.0064449944,0.0011384599,0.00036705387,0.00006569691,0.00008086586,0.0003523963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773407,0.00006202298,0.0004779372,0.00048073567,0.000694275,0.00055098464],"domain_scores_gemma":[0.99744725,0.00097124535,0.000099783036,0.00046377326,0.00018847031,0.00082947785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000875044,0.00021410301,0.00037778149,0.0001921543,0.0001455228,0.00003861617,0.00026397858,0.000109469256,0.0002762389],"category_scores_gemma":[0.0011769987,0.00013927824,0.0002563453,0.00036045755,0.00043584732,0.00010101807,0.00011166758,0.00021670417,0.00003578203],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031763433,0.00028507772,0.0058848206,0.00015771596,0.00002988942,0.000042344345,0.000019452033,3.9824485e-8,0.0056017707,0.0004443178,0.02907663,0.9584262],"study_design_scores_gemma":[0.026045656,0.00041034742,0.035045534,0.016226606,0.00080516725,0.0021940356,0.0002217696,0.5498186,0.027916798,0.007866314,0.33135456,0.0020946397],"about_ca_topic_score_codex":0.000015560254,"about_ca_topic_score_gemma":0.0000025216477,"teacher_disagreement_score":0.95633155,"about_ca_system_score_codex":0.00005614346,"about_ca_system_score_gemma":0.00031522036,"threshold_uncertainty_score":0.5679602},"labels":[],"label_agreement":null},{"id":"W2283114046","doi":"10.1186/s12880-016-0156-6","title":"Sparse reconstruction of compressive sensing MRI using cross-domain stochastically fully connected conditional random fields","year":2016,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Compressed sensing; Computer science; Magnetic resonance imaging; Artificial intelligence; Iterative reconstruction; Computer vision; Data acquisition; Sampling (signal processing); Real-time MRI; Reconstruction algorithm; Process (computing); Image quality; Diffusion MRI; Dynamic contrast-enhanced MRI; Image (mathematics); Radiology; Medicine","score_opus":0.024510520407081814,"score_gpt":0.34485227794023293,"score_spread":0.32034175753315114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283114046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16362093,0.000045292145,0.83427995,0.0010765763,0.000092837654,0.0002902352,0.000022671422,0.00009717517,0.00047431345],"genre_scores_gemma":[0.74902374,0.000015184373,0.2503251,0.00032190676,0.0002258643,0.000009707671,0.000020681744,0.000016949449,0.000040848292],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855983,0.000056033412,0.00047349592,0.00027234992,0.0004026759,0.0002356066],"domain_scores_gemma":[0.9982207,0.00083926803,0.0001916026,0.00022218893,0.00029057803,0.00023567007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026763094,0.00013267159,0.0003311252,0.00008525468,0.00010588555,0.00001094516,0.00007568194,0.000105699626,0.0006879768],"category_scores_gemma":[0.0010711272,0.00009634643,0.000099053555,0.00011668104,0.0006838432,0.00011319295,0.00005492643,0.00018547183,0.0000061898086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033076452,0.0005949616,0.04266932,0.0005270789,0.00017628836,0.00040865585,0.000267065,0.0011302172,0.69423133,0.040024627,0.0029585739,0.21370423],"study_design_scores_gemma":[0.058265522,0.00037828117,0.021849442,0.013182801,0.0006513337,0.015206102,0.001002083,0.5922753,0.13501304,0.15726285,0.0032109222,0.0017023154],"about_ca_topic_score_codex":0.000013985359,"about_ca_topic_score_gemma":0.0000047734934,"teacher_disagreement_score":0.5911451,"about_ca_system_score_codex":0.00006189058,"about_ca_system_score_gemma":0.00029928694,"threshold_uncertainty_score":0.7532863},"labels":[],"label_agreement":null},{"id":"W2515818870","doi":"10.1186/s12880-016-0152-x","title":"Multimodal MRI for early diabetic mild cognitive impairment: study protocol of a prospective diagnostic trial","year":2016,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Stanley Medical Research Institute","keywords":"Cognitive impairment; Protocol (science); Medicine; Cognition; Multimodal therapy; Computer science; Internal medicine; Pathology; Alternative medicine; Psychiatry","score_opus":0.02291675306252773,"score_gpt":0.379629341277537,"score_spread":0.3567125882150093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515818870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7019918,0.00001513054,0.015849028,0.00044212965,0.00011114176,0.28098753,0.000027584798,0.00006414921,0.00051147136],"genre_scores_gemma":[0.73255354,0.0000020107527,0.00030099667,0.00009289221,0.0003035491,0.26642787,0.0000048707266,0.00003261496,0.00028165118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99627584,0.00029495583,0.0006551802,0.0005944019,0.0015021946,0.0006774314],"domain_scores_gemma":[0.9942303,0.0042349193,0.00017023865,0.00023343909,0.00061220326,0.0005189477],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0014518441,0.00026420748,0.0005657778,0.00021683107,0.00010407255,0.00002885451,0.00018272782,0.00007151172,0.00089032226],"category_scores_gemma":[0.009537313,0.00016293496,0.00023550683,0.00023816581,0.00044004596,0.00016633619,0.00016296416,0.00021644287,0.000049893908],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.041160014,0.0056724856,0.9302916,0.00039671644,0.00020868228,0.000086817745,0.00047542388,1.535854e-8,0.0001792833,0.00001480375,0.00023028204,0.021283891],"study_design_scores_gemma":[0.28663886,0.0077959755,0.6987441,0.0017367465,0.00031397192,0.000010206134,0.0007796505,0.00022763161,0.0033047274,0.0002037427,0.00004407478,0.00020027654],"about_ca_topic_score_codex":0.000045295234,"about_ca_topic_score_gemma":0.000015591653,"teacher_disagreement_score":0.24547885,"about_ca_system_score_codex":0.0001059453,"about_ca_system_score_gemma":0.0007869892,"threshold_uncertainty_score":0.99880576},"labels":[],"label_agreement":null},{"id":"W2562125360","doi":"10.1186/s12880-016-0170-8","title":"Skeleton-based cerebrovascular quantitative analysis","year":2016,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ryerson University","keywords":"Skeleton (computer programming); Computer science; Artificial intelligence","score_opus":0.01732141839827223,"score_gpt":0.3113480598542735,"score_spread":0.29402664145600127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562125360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091462705,0.00010873891,0.9934409,0.004152916,0.00015013019,0.0001221776,0.0000017856413,0.00058015034,0.0005285281],"genre_scores_gemma":[0.1712968,0.000025929596,0.8237839,0.004582516,0.00007221523,0.000049944603,0.000006137441,0.000016519907,0.00016606571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672705,0.0003589182,0.00042912766,0.0005818309,0.001503572,0.00039949446],"domain_scores_gemma":[0.9969618,0.001516996,0.00012869279,0.0007467256,0.00015227274,0.0004935685],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013897874,0.00017275149,0.00030598676,0.0004101789,0.00008763963,0.00010037069,0.0011989448,0.00006101546,0.0012503192],"category_scores_gemma":[0.0027496803,0.000111852,0.00028102324,0.0011965091,0.00029882995,0.0005283997,0.00021378689,0.0001382241,0.0001976201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001221874,0.00029492972,0.10072011,0.00010208235,0.00048390313,0.00018275714,0.00029415396,0.000022762824,0.006228526,0.022126677,0.011807709,0.8577242],"study_design_scores_gemma":[0.0035163937,0.000098354096,0.026232095,0.0005638372,0.0003745692,0.000025070283,0.000108362496,0.8851979,0.07536023,0.004542811,0.0028626397,0.001117772],"about_ca_topic_score_codex":0.000057176083,"about_ca_topic_score_gemma":0.00002830855,"teacher_disagreement_score":0.8851751,"about_ca_system_score_codex":0.00008125673,"about_ca_system_score_gemma":0.00041669674,"threshold_uncertainty_score":0.9996627},"labels":[],"label_agreement":null},{"id":"W2569605734","doi":"10.1186/s12880-016-0175-3","title":"CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis","year":2017,"lang":"en","type":"review","venue":"BMC Medical Imaging","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Natural Science Foundation of China","keywords":"Epidermal growth factor receptor; Meta-analysis; Lung cancer; Medicine; Confidence interval; Odds ratio; Publication bias; Oncology; Radiology; Internal medicine; Cancer","score_opus":0.07217960541611954,"score_gpt":0.4073036938472972,"score_spread":0.3351240884311777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569605734","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000053451954,0.9932868,0.0022618354,0.0003640737,0.000072589246,0.0033322463,0.00042291742,0.00002382655,0.0002303846],"genre_scores_gemma":[0.00017018766,0.9948398,0.0014168482,0.00042691568,0.00009485461,0.0014181522,0.00042845696,0.00006229941,0.0011425081],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.9967619,0.00028678813,0.0014271786,0.00055177376,0.00068370195,0.00028862167],"domain_scores_gemma":[0.99605703,0.000600578,0.00189433,0.0007027199,0.0003242945,0.00042103312],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005122918,0.00054182525,0.0088658715,0.00024734906,0.00011401609,0.000052527037,0.0002473326,0.000086168475,0.0010515486],"category_scores_gemma":[0.00073689444,0.00029301902,0.0014207605,0.0003256271,0.00015740871,0.000097295306,0.00006810076,0.00033614447,0.000009152277],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023226426,0.000062294595,0.0003974025,0.9219669,0.07130602,0.00014509737,0.000062326624,2.659774e-8,2.6863352e-8,0.000003116054,0.0001656655,0.00588882],"study_design_scores_gemma":[0.00037066164,0.000019099005,0.000070669055,0.14849281,0.84733963,0.0001714207,0.000012928782,0.00006364295,4.6969862e-7,3.1546577e-7,0.003218902,0.00023946655],"about_ca_topic_score_codex":0.00022252025,"about_ca_topic_score_gemma":0.000038854338,"teacher_disagreement_score":0.7760336,"about_ca_system_score_codex":0.00016191062,"about_ca_system_score_gemma":0.0019235458,"threshold_uncertainty_score":0.9999522},"labels":[],"label_agreement":null},{"id":"W2570913926","doi":"10.1186/s12880-016-0172-6","title":"Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases","year":2017,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Army Medical Research Acquisition Activity; Congressionally Directed Medical Research Programs; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; DOD Peer Reviewed Cancer Research Program; IXICO; Wallace H. Coulter Foundation; H. Lundbeck A/S; National Cancer Institute; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; National Science Foundation; Case Western Reserve University; Bristol-Myers Squibb; Cleveland Clinic; F. Hoffmann-La Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Foundation for the National Institutes of Health; Case Comprehensive Cancer Center, Case Western Reserve University; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Computer science; Workflow; Context (archaeology); Dimensionality reduction; Sensor fusion; Artificial intelligence; Identification (biology); Data mining; Data integration; Machine learning; Modalities; Database","score_opus":0.07334934955420616,"score_gpt":0.36322643589348347,"score_spread":0.28987708633927733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570913926","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58813876,0.005817421,0.32346624,0.07716132,0.0026643563,0.0017256761,0.00012413283,0.00039280002,0.00050927565],"genre_scores_gemma":[0.93476975,0.00019360722,0.060483232,0.0023556594,0.001458539,0.000046310266,0.00050337386,0.00008128179,0.00010825628],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961496,0.00014896077,0.00067134335,0.001259704,0.0010490938,0.0007212883],"domain_scores_gemma":[0.99483836,0.0021866478,0.00033656665,0.001328133,0.0001351061,0.0011751661],"candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002746894,0.000410448,0.00070814206,0.00020339654,0.0013325388,0.0005365092,0.00054146163,0.00011614453,0.00007958692],"category_scores_gemma":[0.01717586,0.0003413526,0.00011828322,0.0000922274,0.0026492067,0.0008002037,0.00082867837,0.0007927487,0.0000042308466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030704107,0.00028888293,0.5075096,0.00081156555,0.00006951345,0.00042404482,0.0001238345,0.000005345252,0.001462423,0.0003031677,0.016588463,0.47210613],"study_design_scores_gemma":[0.005420216,0.000033550896,0.12785053,0.0017589555,0.00029491787,0.0014777812,0.00034779,0.8435017,0.000062010266,0.00032652257,0.018479666,0.00044637124],"about_ca_topic_score_codex":0.00031791936,"about_ca_topic_score_gemma":0.00000824848,"teacher_disagreement_score":0.8434963,"about_ca_system_score_codex":0.00006577327,"about_ca_system_score_gemma":0.00062434026,"threshold_uncertainty_score":0.9999676},"labels":[],"label_agreement":null},{"id":"W2692620580","doi":"10.1186/s12880-017-0209-5","title":"CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma – a quantitative analysis","year":2017,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network; Ontario Institute for Cancer Research; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Sunnybrook Health Science Centre","funders":"Government of Ontario; Ontario Institute for Cancer Research","keywords":"Medicine; Pancreatic ductal adenocarcinoma; Correlation; Receiver operating characteristic; Rank correlation; Tumor grade; Proportional hazards model; Survival analysis; Adenocarcinoma; Mann–Whitney U test; Pancreas; Radiology; Pancreatic cancer; Gastroenterology; Internal medicine; Mathematics; Cancer; Statistics","score_opus":0.0183835349265446,"score_gpt":0.31833538973884257,"score_spread":0.29995185481229797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2692620580","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9820732,0.0013240431,0.0055025457,0.0077946074,0.00021073458,0.00026247578,0.000008758443,0.000095555035,0.0027280888],"genre_scores_gemma":[0.9958671,0.000060748232,0.002002421,0.0010763017,0.0002116532,0.000018695662,0.00008154513,0.000047955586,0.0006336084],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996721,0.00029305564,0.0004313422,0.0006241968,0.0013110982,0.00061928923],"domain_scores_gemma":[0.99737144,0.0008530254,0.00042570583,0.0006611574,0.00012623904,0.000562445],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0013723578,0.00032560318,0.0010465973,0.00037483516,0.0003263741,0.00019289725,0.0004456859,0.0000935043,0.00025341008],"category_scores_gemma":[0.014931227,0.0002360509,0.0002479054,0.00038925323,0.0005931396,0.00022112926,0.00014764478,0.0013279978,0.000010285984],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015513095,0.00016241925,0.99248534,0.00008435901,0.00064448966,0.0043378742,0.00026839547,0.00005015935,0.00004639414,0.00030785255,0.00058500515,0.0008725962],"study_design_scores_gemma":[0.0033594468,0.00005216844,0.9047917,0.0010749549,0.001012332,0.0002508306,0.0004917919,0.08854506,0.000005154803,0.000103972336,0.000056872013,0.0002556872],"about_ca_topic_score_codex":0.0013134399,"about_ca_topic_score_gemma":0.0018980268,"teacher_disagreement_score":0.088494904,"about_ca_system_score_codex":0.0001530131,"about_ca_system_score_gemma":0.00040671419,"threshold_uncertainty_score":0.9933664},"labels":[],"label_agreement":null},{"id":"W2781188568","doi":"10.1186/s12880-017-0236-2","title":"Improving the evaluation of cardiac function in rats at 7T with denoising filters: a comparison study","year":2017,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Hôpital Fleurimont","funders":"Fonds de Recherche du Québec - Santé","keywords":"Noise reduction; Ejection fraction; Segmentation; Computer science; Ventricle; Filter (signal processing); Cardiac function curve; Pattern recognition (psychology); Medicine; Biomedical engineering; Artificial intelligence; Cardiology; Computer vision; Heart failure","score_opus":0.15102675773463192,"score_gpt":0.43331228315720466,"score_spread":0.28228552542257274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781188568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96353096,0.00026256414,0.03350349,0.0010404807,0.00007685655,0.0011106933,9.2445123e-7,0.000054967753,0.00041908876],"genre_scores_gemma":[0.9975274,0.000006910717,0.0020951387,0.00010606549,0.00008841607,0.00013197049,0.000005578685,0.000016877502,0.000021613481],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99830383,0.00013567631,0.00028248373,0.0002647156,0.00085049286,0.0001627923],"domain_scores_gemma":[0.9987073,0.00017476754,0.0002500104,0.00068124,0.00011163924,0.00007504291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015433143,0.000102652266,0.000238891,0.000059802904,0.00026807567,0.000032978594,0.00017729467,0.000023826002,0.000021080396],"category_scores_gemma":[0.0009338242,0.00006720362,0.00004179648,0.00008766915,0.00018297508,0.00012927133,0.00013850193,0.00023724818,0.0000022577165],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001111341,0.00019382405,0.85095215,0.000037790418,0.000013610307,0.000003821389,0.0003057662,0.00003007883,0.006956428,0.000021295571,0.00016504269,0.14120904],"study_design_scores_gemma":[0.0018685068,0.0001402932,0.88046587,0.00044215933,0.00039278966,0.00002339585,0.001336532,0.11074181,0.003979761,0.000115310635,0.00036352105,0.00013006762],"about_ca_topic_score_codex":0.00026234367,"about_ca_topic_score_gemma":0.00018838688,"teacher_disagreement_score":0.14107896,"about_ca_system_score_codex":0.00010929105,"about_ca_system_score_gemma":0.00018149556,"threshold_uncertainty_score":0.27404842},"labels":[],"label_agreement":null},{"id":"W2799605871","doi":"10.1186/s12880-018-0250-z","title":"Dental age estimation in southern Chinese population using panoramic radiographs: validation of three population specific reference datasets","year":2018,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Forensic Anthropology and Bioarchaeology Studies","field":"Arts and Humanities","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Confidence interval; Medicine; Age groups; Demography; Estimation; Population; Chinese population; Significant difference; Reference values; Mean difference; Dentistry; Orthodontics; Biology","score_opus":0.05093710642457559,"score_gpt":0.3179184450607743,"score_spread":0.2669813386361987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799605871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99345595,0.00017784399,0.0053668316,0.00012620224,0.00049549376,0.00012435527,0.00005403773,0.00003488698,0.00016439366],"genre_scores_gemma":[0.9971296,0.0000119949555,0.0014213833,0.000040118004,0.00025864376,0.0000026725302,0.0011172683,0.00000895069,0.000009346274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99887973,0.00010323518,0.0003735987,0.00021246391,0.00025350656,0.00017744712],"domain_scores_gemma":[0.999521,0.00009765151,0.0001605025,0.0001408916,0.000045119203,0.00003482364],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00035169895,0.00012332376,0.00020498819,0.00020721996,0.00023485039,0.000019774687,0.00011149399,0.000060873685,0.0005526345],"category_scores_gemma":[0.00014676621,0.000098251105,0.000038019596,0.000096431235,0.0037910284,0.00024047373,0.00007263488,0.0001230053,0.000016226059],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004046082,0.000040670653,0.981939,0.000029881116,0.000012102069,0.000007576173,0.0028782974,0.000016239715,0.000023701206,0.011535253,0.00006396068,0.0034128171],"study_design_scores_gemma":[0.0013695641,0.00008497134,0.8747611,0.0004296434,0.00006988574,0.000038085727,0.015617048,0.08208916,0.00006153476,0.024958044,0.00009335957,0.00042757872],"about_ca_topic_score_codex":0.02365909,"about_ca_topic_score_gemma":0.14817734,"teacher_disagreement_score":0.124518245,"about_ca_system_score_codex":0.00003213934,"about_ca_system_score_gemma":0.000017011505,"threshold_uncertainty_score":0.9989201},"labels":[],"label_agreement":null},{"id":"W2804729489","doi":"10.1186/s12880-018-0258-4","title":"MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection","year":2018,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"Canada Research Chairs; Cancer Care Ontario","keywords":"Computer science; Voxel; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Prostate cancer; Radiomics; Magnetic resonance imaging; Cancer; Radiology; Medicine","score_opus":0.02054364746163219,"score_gpt":0.34463233395024545,"score_spread":0.3240886864886133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804729489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19245347,0.0022816153,0.8006726,0.0021945322,0.0006379664,0.0012803143,0.000031598232,0.00041900814,0.000028924722],"genre_scores_gemma":[0.9436759,0.0010885465,0.051977023,0.0018500467,0.00068379246,0.00058143894,0.00004299649,0.000045872985,0.000054400793],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988643,0.000031939166,0.00024053415,0.0003412497,0.00023827364,0.00028370446],"domain_scores_gemma":[0.9991753,0.00015044551,0.000082718834,0.00014454735,0.00015298072,0.0002940239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017222187,0.00014503268,0.0002346468,0.00007198885,0.00015247315,0.000034294608,0.00004310863,0.00009332207,0.00007441393],"category_scores_gemma":[0.00037499052,0.00011766442,0.000049536724,0.00015167765,0.00022916538,0.00008780513,0.00003343636,0.000111798414,0.000007598764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006334131,0.00042188336,0.67550594,0.0009083109,0.00013146552,0.000033924767,0.0020402991,0.000049596372,0.002327546,0.00011323914,0.003358008,0.31447634],"study_design_scores_gemma":[0.0049403994,0.00022140106,0.03992856,0.0012915831,0.0002015942,0.000050680694,0.00016084021,0.93743587,0.011828488,0.0002481245,0.0035032928,0.0001891436],"about_ca_topic_score_codex":0.00014793788,"about_ca_topic_score_gemma":0.00031385655,"teacher_disagreement_score":0.9373863,"about_ca_system_score_codex":0.00018483409,"about_ca_system_score_gemma":0.00023401387,"threshold_uncertainty_score":0.4798216},"labels":[],"label_agreement":null},{"id":"W2805284316","doi":"10.1186/s12880-018-0255-7","title":"An automated algorithm for the detection of cortical interruptions and its underlying loss of trabecular bone; a reproducibility study","year":2018,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Rheumatoid Arthritis Research and Therapies","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Pfizer","keywords":"Reproducibility; Reliability (semiconductor); Medicine; Nuclear medicine; Operator (biology); Algorithm; Biomedical engineering; Volume (thermodynamics); Joint (building); Computer science; Mathematics; Statistics; Physics","score_opus":0.0549177939685825,"score_gpt":0.4022560444642369,"score_spread":0.3473382504956544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805284316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8442056,0.0017419385,0.15295403,0.00026108598,0.00007346028,0.00068353274,0.0000043952296,0.00006879916,0.000007150817],"genre_scores_gemma":[0.9982017,0.00019473926,0.0014413837,0.000031815573,0.00006710389,0.000045158667,0.0000027227675,0.000011496892,0.000003843349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984214,0.0002004586,0.0003804404,0.00036279194,0.00044411505,0.00019084805],"domain_scores_gemma":[0.99856746,0.0004447277,0.000066720524,0.0004560547,0.0002868125,0.00017823649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025591224,0.00007961527,0.0002478776,0.00006874953,0.00014757135,0.000014412499,0.00008671026,0.000033023243,0.00004564312],"category_scores_gemma":[0.003765391,0.00005332032,0.00005611967,0.00013894771,0.0005858809,0.000090165486,0.000042546544,0.00015630173,9.312477e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005638196,0.0018066539,0.057329595,0.000298932,0.00024566284,0.000029900159,0.004532524,7.9518645e-7,0.08357683,0.00010475257,0.00002357211,0.851487],"study_design_scores_gemma":[0.0033768066,0.0013630952,0.16687614,0.0003312951,0.000042063275,0.00021843959,0.004750987,0.8125723,0.010173974,0.00016060092,0.00005010017,0.00008419015],"about_ca_topic_score_codex":0.000098743665,"about_ca_topic_score_gemma":0.00015980878,"teacher_disagreement_score":0.85140276,"about_ca_system_score_codex":0.000016411306,"about_ca_system_score_gemma":0.00014944411,"threshold_uncertainty_score":0.45077968},"labels":[],"label_agreement":null},{"id":"W2891483161","doi":"10.1186/s12880-018-0266-4","title":"Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study","year":2018,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; NeuroRx Research (Canada)","funders":"Canadian Institutes of Health Research; National Institute on Aging; Alzheimer's Disease Neuroimaging Initiative","keywords":"Image quality; Computer science; Artificial intelligence; Metric (unit); Feature (linguistics); Contrast (vision); Quality Score; Distortion (music); Quality (philosophy); Pattern recognition (psychology); Protocol (science); Neuroimaging; Grayscale; Similarity (geometry); Standardization; Computer vision; Data mining; Image (mathematics); Medicine; Pathology","score_opus":0.05769380088286784,"score_gpt":0.40249826487826995,"score_spread":0.3448044639954021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891483161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08312602,0.00096778484,0.9118269,0.0016867206,0.0009845858,0.0009846729,0.000018890958,0.00026296556,0.00014147568],"genre_scores_gemma":[0.9231344,0.0000033831109,0.07525583,0.0011891185,0.00022637917,0.00009286038,0.0000036410852,0.000025512403,0.000068888745],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99460316,0.0013219098,0.0009885974,0.000984322,0.0013162876,0.0007857435],"domain_scores_gemma":[0.9969049,0.0014817861,0.00019774419,0.0008579045,0.0002874691,0.00027018972],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005001734,0.00030260868,0.00054921897,0.00035640527,0.00025898727,0.00044154274,0.0013660673,0.000049511076,0.000019149975],"category_scores_gemma":[0.0028751646,0.00026572403,0.00013716744,0.0008837073,0.00023120573,0.00080598437,0.00063945336,0.00031987962,0.000016229094],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027566854,0.0013766133,0.6437999,0.0009111801,0.00003215447,0.0004890647,0.0064767585,0.00000599299,0.0011108611,0.003435899,0.0040001282,0.33808577],"study_design_scores_gemma":[0.023380047,0.0003551981,0.5628939,0.000552006,0.000027679958,0.000115854265,0.004196115,0.40369958,0.0011787512,0.0003765508,0.0022703288,0.00095395936],"about_ca_topic_score_codex":0.00027051396,"about_ca_topic_score_gemma":0.00019032096,"teacher_disagreement_score":0.8400084,"about_ca_system_score_codex":0.00022189684,"about_ca_system_score_gemma":0.00038822985,"threshold_uncertainty_score":0.9999795},"labels":[],"label_agreement":null},{"id":"W2915354137","doi":"10.1186/s12880-019-0316-6","title":"CoreSlicer: a web toolkit for analytic morphomics","year":2019,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; McGill University Health Centre; McGill University","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada","keywords":"Computer science; DICOM; Software; Interface (matter); Plug-in; Segmentation; Graphical user interface; Medical imaging; Medical physics; Artificial intelligence; Medicine","score_opus":0.032885420886444895,"score_gpt":0.35991943239090923,"score_spread":0.3270340115044643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915354137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8410178,0.0029512728,0.06631804,0.051572233,0.0028047194,0.0030472341,0.000033754735,0.0005959203,0.031659026],"genre_scores_gemma":[0.94912326,0.00023813293,0.01820556,0.028480815,0.0006729355,0.000064543674,0.000052805648,0.00004892785,0.0031129979],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844843,0.00002753678,0.00036411604,0.00028505537,0.0004797913,0.0003950459],"domain_scores_gemma":[0.99861616,0.0004436581,0.00007068897,0.00027051705,0.000101169964,0.00049781316],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00064490753,0.00011883017,0.00034391406,0.00013890279,0.0000638794,0.000023731029,0.00011616947,0.00007180445,0.0019607432],"category_scores_gemma":[0.00072902825,0.00010415108,0.00013407196,0.0001577986,0.00006802352,0.000067069814,0.000042424654,0.00026750792,0.00032121508],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062313786,0.00036020097,0.86592436,0.004447042,0.00006032272,0.00010876356,0.00017155959,0.0000043338996,0.0027569411,0.004734949,0.09590054,0.024907833],"study_design_scores_gemma":[0.017326035,0.0001604086,0.030399466,0.0026781303,0.0001900123,0.0006007058,0.0005764381,0.6095758,0.00033420412,0.0011170981,0.336605,0.00043669055],"about_ca_topic_score_codex":0.000015324948,"about_ca_topic_score_gemma":0.000013438876,"teacher_disagreement_score":0.8355249,"about_ca_system_score_codex":0.00009734321,"about_ca_system_score_gemma":0.0007321256,"threshold_uncertainty_score":0.9989516},"labels":[],"label_agreement":null},{"id":"W2970438258","doi":"10.1186/s12880-019-0367-8","title":"Value of virtual monochromatic spectral image of dual-layer spectral detector CT with noise reduction algorithm for image quality improvement in obese simulated body phantom","year":2019,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto General Hospital","funders":"","keywords":"Imaging phantom; Monochromatic color; Image quality; Image noise; Nuclear medicine; Contrast-to-noise ratio; Detector; Noise (video); Iterative reconstruction; Noise reduction; Physics; Optics; Computer science; Materials science; Medicine; Artificial intelligence; Image (mathematics)","score_opus":0.00756610359810841,"score_gpt":0.27364382100055007,"score_spread":0.2660777174024417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970438258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.686982,0.00011790713,0.31181678,0.00002169222,0.00024826737,0.00057157985,0.000022297705,0.00011292721,0.00010657987],"genre_scores_gemma":[0.9406761,0.000019646246,0.05899092,0.000015386968,0.00014896248,0.000027724785,0.000021587095,0.00007093916,0.000028743198],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733925,0.00008059819,0.0009307739,0.00040548397,0.0006392072,0.00060467864],"domain_scores_gemma":[0.99882936,0.00030316584,0.00020136383,0.00037647327,0.0000964399,0.00019317868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070606486,0.00033380112,0.00064573967,0.0002143238,0.00003458694,0.000025492634,0.000213534,0.00005400939,0.0001481182],"category_scores_gemma":[0.00020229944,0.0003056372,0.00015011849,0.00033414143,0.0001898715,0.00057488197,0.000053512482,0.00035267873,0.000008733969],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014976172,0.000249897,0.0015423935,0.0010987396,0.00008931368,0.000048382186,0.000519171,0.014097727,0.9604982,0.00009384301,0.000015476757,0.021597084],"study_design_scores_gemma":[0.0029061863,0.00012929733,0.0024359785,0.0003944176,0.000040993396,0.000038478945,0.00076240447,0.6327947,0.3599965,0.00021163441,0.000004160812,0.00028523727],"about_ca_topic_score_codex":0.00014624611,"about_ca_topic_score_gemma":0.000017091508,"teacher_disagreement_score":0.618697,"about_ca_system_score_codex":0.00018826108,"about_ca_system_score_gemma":0.00014502398,"threshold_uncertainty_score":0.99993956},"labels":[],"label_agreement":null},{"id":"W2994935954","doi":"10.1186/s12880-019-0393-6","title":"Inter and intra-reliability of ultrasonography for the measurement of abdominal subcutaneous & visceral adipose tissue thickness at 12 weeks gestation","year":2019,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Gestational Diabetes Research and Management","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"Health Research Institute, University of Limerick; University of Limerick","keywords":"Adipose tissue; Medicine; Subcutaneous adipose tissue; Pregnancy; Ultrasound; Population; Coefficient of variation; Ultrasonography; Gestation; Nuclear medicine; Limits of agreement; Obstetrics; Radiology; Internal medicine; Mathematics; Biology; Statistics","score_opus":0.01971971132202085,"score_gpt":0.2992640062372978,"score_spread":0.279544294915277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994935954","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94330686,0.00058764877,0.05124899,0.002643914,0.00016863353,0.0014426714,0.000017766908,0.000015310501,0.0005681914],"genre_scores_gemma":[0.99491787,0.000089085064,0.004445716,0.00024117384,0.000043334654,0.000054300777,0.00003106836,0.000011261104,0.00016618885],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981568,0.000081171995,0.000342768,0.000218005,0.00096813636,0.00023315307],"domain_scores_gemma":[0.99826705,0.00087607856,0.00010000341,0.00022904278,0.0003583198,0.00016952696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001532058,0.00010454553,0.00023867839,0.000090514215,0.000042407228,0.000010968858,0.0001141766,0.000036725214,0.0003608839],"category_scores_gemma":[0.0015548608,0.00006914969,0.000080212725,0.000083551655,0.00029287426,0.000054806624,0.00007044372,0.00013891427,0.0000069129483],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006122692,0.00089301117,0.48300308,0.013284689,0.00048255912,0.000019665555,0.0025183004,0.00014628895,0.08240102,0.0009495824,0.0064757923,0.4037033],"study_design_scores_gemma":[0.011686695,0.0016722846,0.88463145,0.0026603353,0.0008257709,0.0001746171,0.0018714515,0.05429959,0.015403236,0.0020364325,0.024221634,0.0005164819],"about_ca_topic_score_codex":0.00010774666,"about_ca_topic_score_gemma":0.00015672413,"teacher_disagreement_score":0.40318683,"about_ca_system_score_codex":0.000061207524,"about_ca_system_score_gemma":0.00014139536,"threshold_uncertainty_score":0.39514253},"labels":[],"label_agreement":null},{"id":"W3006031087","doi":"10.1186/s12880-020-0413-6","title":"Quantitative comparison of two-dimensional and three-dimensional strain measurement using MRI feature tracking in repair Fontan patients and normal child volunteers","year":2020,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Circle Cardiovascular Imaging","funders":"National Key Research and Development Program of China; School of Medicine, Shanghai Jiao Tong University; Shanghai Jiao Tong University","keywords":"Intraclass correlation; Feature tracking; Medicine; Ventricle; Repeatability; Strain (injury); Ejection fraction; Cardiac magnetic resonance; Cardiac function curve; Magnetic resonance imaging; Coefficient of variation; Cardiology; Nuclear medicine; Fontan procedure; Internal medicine; Cardiac magnetic resonance imaging; Heart failure; Radiology","score_opus":0.05238624792060589,"score_gpt":0.3365732805974164,"score_spread":0.28418703267681056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006031087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99098,0.0060204077,0.00041496943,0.0019892526,0.000078461366,0.00038739864,0.000025812189,0.000038818685,0.00006482926],"genre_scores_gemma":[0.99159175,0.000003977563,0.007410398,0.0008927614,0.000062343286,0.000004163608,0.000015399448,0.000018274292,9.4660317e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99761146,0.00008824751,0.00042344135,0.0003798081,0.001221813,0.00027524855],"domain_scores_gemma":[0.9990559,0.00015320191,0.000075814336,0.00008418056,0.00022305555,0.00040781716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042086758,0.00019550246,0.00049330125,0.000095983174,0.00012828378,0.0000118015705,0.000042043317,0.00004889531,0.00003122115],"category_scores_gemma":[0.0010842545,0.00017281696,0.00008171103,0.00016703561,0.00036426098,0.0001241897,0.00016086201,0.00037840576,8.534441e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061660825,0.00024891205,0.9873002,0.00040775532,0.000081488324,0.000074822696,0.0012989755,0.000078514895,0.00090336887,0.000027471917,0.00033905215,0.008622829],"study_design_scores_gemma":[0.0036857114,0.0001625704,0.5630546,0.0010626867,0.00013100279,0.000029442714,0.0006139168,0.4309972,0.00007285095,0.000014386288,0.000024944833,0.00015066414],"about_ca_topic_score_codex":0.00014783411,"about_ca_topic_score_gemma":0.0012151323,"teacher_disagreement_score":0.43091866,"about_ca_system_score_codex":0.00007574137,"about_ca_system_score_gemma":0.00028028636,"threshold_uncertainty_score":0.7047271},"labels":[],"label_agreement":null},{"id":"W3010245316","doi":"10.1186/s12880-020-0418-1","title":"CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging","year":2020,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Pancreatic and Hepatic Oncology Research","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Juravinski Hospital; University of Toronto; Hamilton Health Sciences; Sunnybrook Health Science Centre; Sinai Health System; Health Sciences Centre; Lunenfeld-Tanenbaum Research Institute","funders":"Government of Ontario; Ontario Institute for Cancer Research","keywords":"Convolutional neural network; Radiomics; Pancreatic ductal adenocarcinoma; Proportional hazards model; Artificial intelligence; Computer science; Survival analysis; Hazard ratio; Deep learning; Medicine; Overall survival; Pattern recognition (psychology); Radiology; Oncology; Internal medicine; Pancreatic cancer; Cancer","score_opus":0.06929486105858222,"score_gpt":0.37336654047504203,"score_spread":0.3040716794164598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010245316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5002465,0.0018212192,0.43510166,0.05872547,0.00021144532,0.0010593247,0.000012807835,0.00018585574,0.0026357207],"genre_scores_gemma":[0.9754169,0.00006272111,0.011760058,0.011465686,0.00079567195,0.00018462898,0.00007542853,0.000066009285,0.00017287601],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953316,0.00024079853,0.0008046892,0.0006950929,0.0019734486,0.00095436565],"domain_scores_gemma":[0.9961818,0.0017294701,0.000094070056,0.00033419655,0.00015023084,0.0015102479],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001986943,0.00030200972,0.0010468161,0.00019627163,0.00011280524,0.000029071382,0.00041974982,0.00019478184,0.0013573202],"category_scores_gemma":[0.01228124,0.00025754533,0.00021918517,0.00036631917,0.00054610573,0.00010873,0.00016121852,0.0006928315,0.000073602016],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064461795,0.00022759428,0.9880051,0.000713091,0.0000428403,0.0014524852,0.00035672187,0.000033864966,0.00014651236,0.00024749327,0.0035490117,0.004580668],"study_design_scores_gemma":[0.0077611473,0.00007023763,0.076579414,0.00049145444,0.00016505294,0.00015845771,0.00039201914,0.9137814,0.000026067893,0.00018606242,0.00016795122,0.00022070223],"about_ca_topic_score_codex":0.00012186149,"about_ca_topic_score_gemma":0.000074948664,"teacher_disagreement_score":0.91374755,"about_ca_system_score_codex":0.00017513298,"about_ca_system_score_gemma":0.0041719293,"threshold_uncertainty_score":0.99998766},"labels":[],"label_agreement":null},{"id":"W3014710192","doi":"10.1186/s12880-020-00437-8","title":"The utility of multi-stack alignment and 3D longitudinal image registration to assess bone remodeling in rheumatoid arthritis patients from second generation HR-pQCT scans","year":2020,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Rheumatoid Arthritis Research and Therapies","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Arthritis Society; University of Calgary","keywords":"Medicine; Quantitative computed tomography; Rheumatoid arthritis; Image registration; Nuclear medicine; Radiology; Artifact (error); Bone remodeling; Computer science; Osteoporosis; Pathology; Internal medicine; Bone density; Image (mathematics); Artificial intelligence","score_opus":0.06589886400242496,"score_gpt":0.3199375459629102,"score_spread":0.25403868196048524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014710192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94027865,0.0048766676,0.051683467,0.0024155583,0.00008341428,0.00048335447,0.00003626093,0.000024116503,0.00011854222],"genre_scores_gemma":[0.98491,0.0060209692,0.008545501,0.0003078806,0.00007477168,0.000027670349,0.00009000438,0.000016404685,0.000006764409],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976102,0.00020014084,0.00064481876,0.00038850162,0.00084899476,0.0003073485],"domain_scores_gemma":[0.99882054,0.00024821697,0.00010874198,0.00021973002,0.00016194617,0.00044083517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079287303,0.00014634666,0.00032940484,0.00005444951,0.00013826285,0.0000904379,0.000104614875,0.000048327216,0.00024288853],"category_scores_gemma":[0.0021016141,0.000118101,0.00004876819,0.00013658454,0.00021319982,0.00021910858,0.00009688407,0.00024895137,0.0000063886164],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052476017,0.0003419948,0.6789197,0.00015692014,0.000050614795,0.00009195668,0.0021640002,0.000017314802,0.025105353,0.000056800993,0.0028497565,0.2897209],"study_design_scores_gemma":[0.0076503903,0.00017680589,0.2894912,0.0013389258,0.0000044661456,0.000032032804,0.0010463619,0.69329065,0.0052495315,0.00009380567,0.0013782125,0.00024760247],"about_ca_topic_score_codex":0.00043315976,"about_ca_topic_score_gemma":0.0031645861,"teacher_disagreement_score":0.69327337,"about_ca_system_score_codex":0.00003969984,"about_ca_system_score_gemma":0.00024215542,"threshold_uncertainty_score":0.4816019},"labels":[],"label_agreement":null},{"id":"W3042207345","doi":"10.1186/s12880-020-00479-y","title":"Discriminative pattern of reduced cerebral blood flow in Parkinson’s disease and Parkinsonism-Plus syndrome: an ASL-MRI study","year":2020,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cerebral blood flow; Medicine; Parkinsonism; Cardiology; Receiver operating characteristic; Supplementary motor area; SMA*; Superior frontal gyrus; Parkinson's disease; Internal medicine; Thalamus; Caudate nucleus; Nuclear medicine; Magnetic resonance imaging; Radiology; Disease; Functional magnetic resonance imaging","score_opus":0.027016062232306255,"score_gpt":0.2940367576338295,"score_spread":0.2670206954015233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042207345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9944852,0.00071420235,0.00184023,0.0016455005,0.000115904164,0.0009711641,0.00006617097,0.00007929125,0.000082338345],"genre_scores_gemma":[0.9974878,0.00006555982,0.0011678643,0.00089840335,0.00011654481,0.00011985073,0.00007687741,0.000050862043,0.000016262466],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968571,0.00032572515,0.00053897646,0.000838666,0.0009786783,0.00046087382],"domain_scores_gemma":[0.9975198,0.00012969217,0.00013664145,0.00036728638,0.000058588852,0.0017879826],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035193848,0.00034673046,0.0007145209,0.00015314188,0.000077373574,0.000036362304,0.00020220237,0.00006374665,0.0002733493],"category_scores_gemma":[0.000499668,0.00029085757,0.000114423434,0.00026271332,0.00016906131,0.00021870752,0.0001947044,0.00030779542,0.000010108835],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062665844,0.003384391,0.9566413,0.0004782446,0.00021837787,0.007966546,0.0040720836,0.000006791604,0.00014201557,0.000014284215,0.000068908914,0.026380407],"study_design_scores_gemma":[0.013086495,0.00071950915,0.93448126,0.0005466391,0.0009428496,0.00013228146,0.0034326836,0.0458209,0.00024434263,0.00010173966,0.00017078391,0.00032049688],"about_ca_topic_score_codex":0.00023521276,"about_ca_topic_score_gemma":0.0001054338,"teacher_disagreement_score":0.045814108,"about_ca_system_score_codex":0.0000683372,"about_ca_system_score_gemma":0.0005233548,"threshold_uncertainty_score":0.99995434},"labels":[],"label_agreement":null},{"id":"W3084642060","doi":"10.1186/s12880-020-00507-x","title":"Sentinel lymph node mapping using ICG fluorescence and cone beam CT – a feasibility study in a rabbit model of oral cancer","year":2020,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Head and Neck Cancer Studies","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Princess Margaret Cancer Foundation","keywords":"Indocyanine green; Sentinel lymph node; Medicine; Biopsy; Radiology; Head and neck cancer; Fluorescence-lifetime imaging microscopy; Nuclear medicine; Cancer; Surgery; Radiation therapy; Breast cancer; Fluorescence","score_opus":0.12190408895194654,"score_gpt":0.37088084695350865,"score_spread":0.2489767580015621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084642060","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99230605,0.0012573614,0.0034132085,0.0024107024,0.00005516432,0.0004104614,0.000006146553,0.000038003473,0.000102907165],"genre_scores_gemma":[0.99565774,0.00010206326,0.002617649,0.0014923848,0.00008050706,0.000020824493,0.0000017731668,0.000017381142,0.00000966082],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807256,0.000079296165,0.0004969344,0.0004457635,0.00057466904,0.00033075357],"domain_scores_gemma":[0.9992175,0.00011383425,0.00008634234,0.0001676819,0.00007755255,0.0003370914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005777927,0.00017274616,0.00068831956,0.00007301465,0.00005985798,0.000010461097,0.000094347764,0.000027866867,0.00003721481],"category_scores_gemma":[0.0008587289,0.00014746009,0.000055965553,0.00028271938,0.00024300085,0.00008594847,0.00021071643,0.00023409232,0.0000010349339],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014540686,0.00024456324,0.9790606,0.0006070433,0.000032474127,0.00010315953,0.001966578,0.000091447466,0.015526692,0.000002081069,0.00006008111,0.002159871],"study_design_scores_gemma":[0.005166377,0.000017929518,0.3585675,0.0009915914,0.00007647157,0.000021740894,0.0019073747,0.6326354,0.00046041326,0.000010201943,0.000006846055,0.00013815176],"about_ca_topic_score_codex":0.0009856942,"about_ca_topic_score_gemma":0.00010807528,"teacher_disagreement_score":0.6325439,"about_ca_system_score_codex":0.000110796565,"about_ca_system_score_gemma":0.0007157476,"threshold_uncertainty_score":0.6013248},"labels":[],"label_agreement":null},{"id":"W3148916450","doi":"10.1186/s12880-021-00592-6","title":"The prognosis prediction significance of Hounsfield unit value for stroke patients treated by intravenous thrombolysis","year":2021,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Jiangsu Commission of Health","keywords":"Medicine; Hounsfield scale; Thrombolysis; Stroke (engine); Receiver operating characteristic; Internal medicine; Cerebral infarction; Cardiology; Area under the curve; Radiology; Computed tomography; Myocardial infarction; Ischemia","score_opus":0.013438635269171086,"score_gpt":0.2642955655802235,"score_spread":0.2508569303110524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148916450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.894623,0.0017964601,0.08463975,0.0059296964,0.0007331802,0.0024969617,0.0003045972,0.00020822023,0.00926813],"genre_scores_gemma":[0.99508256,0.00029063592,0.0018941119,0.0006485067,0.00018043218,0.00022720748,0.00030368404,0.00003238793,0.0013404787],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980326,0.0000812,0.0004609992,0.0003215582,0.00078389014,0.00031972595],"domain_scores_gemma":[0.99837744,0.00068395166,0.00014731432,0.00032202838,0.000297464,0.00017178219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038435977,0.0001508063,0.0003056768,0.000044048964,0.0001227218,0.00002318162,0.00016720119,0.0000765022,0.00010052823],"category_scores_gemma":[0.0017716291,0.00010953375,0.00013955314,0.00022319384,0.00018453706,0.000053222553,0.000083310006,0.00019210039,0.0000029614607],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020462016,0.00080959196,0.7901022,0.00046809262,0.0005139689,0.000019137382,0.00021215825,0.00000331894,0.00966021,0.00013030127,0.10992022,0.08795623],"study_design_scores_gemma":[0.020551732,0.0012249568,0.3248437,0.0021420654,0.004415984,0.000065823755,0.0049701123,0.04656548,0.45268193,0.00011850606,0.14151688,0.0009028361],"about_ca_topic_score_codex":0.000057856327,"about_ca_topic_score_gemma":0.000009784827,"teacher_disagreement_score":0.46525848,"about_ca_system_score_codex":0.000059389473,"about_ca_system_score_gemma":0.00024149152,"threshold_uncertainty_score":0.44666567},"labels":[],"label_agreement":null},{"id":"W4205196630","doi":"10.1186/s12880-021-00729-7","title":"Deep learning-based pancreas volume assessment in individuals with type 1 diabetes","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Pancreatitis Pathology and Treatment","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; Juvenile Diabetes Research Foundation Canada; Juvenile Diabetes Research Foundation International","keywords":"Pancreas; Magnetic resonance imaging; Medicine; Diabetes mellitus; Volume (thermodynamics); Convolutional neural network; Type 2 diabetes; Artificial intelligence; Computer science; Radiology; Nuclear medicine; Internal medicine; Endocrinology","score_opus":0.01060027862792575,"score_gpt":0.28404593594763916,"score_spread":0.27344565731971343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205196630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99128854,0.0013808333,0.0025293806,0.0016176783,0.00012971567,0.00030596607,0.0000034038121,0.000109296605,0.002635193],"genre_scores_gemma":[0.9932286,0.000018372879,0.0044845366,0.001497524,0.00005550854,0.00020524542,0.00016809536,0.000024436642,0.00031765777],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979632,0.00040292623,0.00022701742,0.00032042063,0.0007105426,0.00037589294],"domain_scores_gemma":[0.99915665,0.00031522606,0.00007464198,0.0001966771,0.00003398834,0.00022281501],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007952633,0.00015114553,0.00035120925,0.0001611055,0.00016316274,0.00001083669,0.00010293068,0.000043317905,0.0028604642],"category_scores_gemma":[0.00042312895,0.0001230219,0.000046044574,0.00031493488,0.00014523116,0.00004208468,0.00008791511,0.00073596794,0.000032930184],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043000695,0.00034898048,0.993479,0.000039015053,0.000042131633,0.00086979877,0.00008195889,0.00042970295,0.000025223611,0.00003656086,0.00018986336,0.0044147708],"study_design_scores_gemma":[0.0038507294,0.000434744,0.95217144,0.00014483111,0.00012751493,0.00014886889,0.00024826068,0.039746143,0.000034319197,0.000034552133,0.0029193666,0.00013922558],"about_ca_topic_score_codex":0.00005639036,"about_ca_topic_score_gemma":0.000045352237,"teacher_disagreement_score":0.041307546,"about_ca_system_score_codex":0.00019129502,"about_ca_system_score_gemma":0.0007630132,"threshold_uncertainty_score":0.99805105},"labels":[],"label_agreement":null},{"id":"W4220973076","doi":"10.1186/s12880-022-00769-7","title":"Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Anhui University; Anhui University of Science and Technology; National Natural Science Foundation of China","keywords":"Hyperintensity; Neuropsychology; Cognition; Neuroimaging; Medicine; Magnetic resonance imaging; White matter; Functional magnetic resonance imaging; Internal medicine; Cardiology; Psychiatry; Radiology","score_opus":0.023822635620543445,"score_gpt":0.22096319518926716,"score_spread":0.19714055956872373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220973076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97705346,0.000042745294,0.0019350579,0.019433191,0.00066214794,0.0006041901,0.00006482137,0.00010027304,0.00010412525],"genre_scores_gemma":[0.9619698,6.390324e-7,0.000072330906,0.037305392,0.00010870859,0.00021060508,0.000035149795,0.000028446379,0.00026892862],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970348,0.00044395102,0.0003130455,0.0006830532,0.0010210802,0.0005040866],"domain_scores_gemma":[0.9974664,0.0017819512,0.00008617755,0.0001693032,0.00016361351,0.00033259654],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0005239507,0.00021001244,0.00026935487,0.000088354005,0.00048655132,0.000042803553,0.00019063486,0.000022528464,0.0006908843],"category_scores_gemma":[0.010247921,0.00017340122,0.000060942104,0.00052994344,0.00023882129,0.00016353467,0.0008144472,0.00045386207,0.00007233342],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027663922,0.00028581885,0.9893603,0.000022390806,0.0000127123385,0.00007911912,0.0001345989,0.0019065215,0.000024465795,0.00002569867,0.005192875,0.00018913264],"study_design_scores_gemma":[0.0028937832,0.00008848839,0.98969686,0.00012282551,0.000021106836,0.000018070816,0.00014219277,0.00615389,0.000023200217,0.00012113378,0.00047826173,0.0002401636],"about_ca_topic_score_codex":0.000037047037,"about_ca_topic_score_gemma":0.00008746066,"teacher_disagreement_score":0.017872203,"about_ca_system_score_codex":0.0001632129,"about_ca_system_score_gemma":0.00027188277,"threshold_uncertainty_score":0.9980892},"labels":[],"label_agreement":null},{"id":"W4224229663","doi":"10.1186/s12880-022-00798-2","title":"Characterizing and quantifying low-value diagnostic imaging internationally: a scoping review","year":2022,"lang":"en","type":"review","venue":"BMC Medical Imaging","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":92,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Norges Forskningsråd","keywords":"Computer science; Value (mathematics); Data science; Medical imaging; Medical physics; Medicine; Artificial intelligence; Machine learning","score_opus":0.6908631566959148,"score_gpt":0.6274841181132064,"score_spread":0.06337903858270844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224229663","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001630975,0.98286206,0.0013807795,0.0061807446,0.0034673943,0.004716794,0.000050344257,0.00033240792,0.0009931868],"genre_scores_gemma":[0.000010184993,0.9647249,0.0007191636,0.030022576,0.0015325695,0.002287239,0.00040243653,0.0002233355,0.000077574165],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.97749805,0.013476818,0.0039855116,0.0014415277,0.0021897643,0.0014083402],"domain_scores_gemma":[0.92840093,0.06630182,0.0030569497,0.00095698,0.00021272794,0.0010705913],"candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.019239366,0.00083089754,0.0033208218,0.000516351,0.0016393646,0.00011798867,0.001133123,0.00029585083,0.005413308],"category_scores_gemma":[0.11432597,0.00076074636,0.00045644614,0.0006639901,0.00026493217,0.0007987234,0.0022715635,0.0057066265,0.00042949186],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021521132,0.000029962057,0.0020528068,0.4450035,0.00003359459,0.00019335747,0.00021239508,1.2141336e-8,8.5988006e-8,0.00027992402,0.00046697212,0.5517252],"study_design_scores_gemma":[0.00020443932,0.0000036823465,0.00007421024,0.54523766,0.00032472302,0.00016280841,0.00022904709,0.00011656751,1.39912215e-8,0.000016029571,0.45322764,0.00040318118],"about_ca_topic_score_codex":0.0005381567,"about_ca_topic_score_gemma":0.00017387922,"teacher_disagreement_score":0.55132204,"about_ca_system_score_codex":0.00093649724,"about_ca_system_score_gemma":0.010001229,"threshold_uncertainty_score":0.9996604},"labels":[],"label_agreement":null},{"id":"W4225655971","doi":"10.1186/s12880-022-00783-9","title":"Distribution of FFRCT in single obstructive coronary stenosis and predictors for major adverse cardiac events: a propensity score matching study","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Fractional flow reserve; Mace; Conventional PCI; Percutaneous coronary intervention; Internal medicine; Cardiology; Propensity score matching; Stenosis; Angina; Myocardial infarction; Coronary angiography","score_opus":0.023818400150727496,"score_gpt":0.2733432079170906,"score_spread":0.2495248077663631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225655971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949739,0.000535708,0.002033964,0.00021990086,0.0004787422,0.0014398192,0.00022171464,0.00004475397,0.00005147492],"genre_scores_gemma":[0.9990179,0.000008480132,0.00033820348,0.00010045779,0.00009818833,0.00019006697,0.000204008,0.000023537083,0.0000191817],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980081,0.00023757415,0.00039937734,0.00036085528,0.0007169297,0.00027717015],"domain_scores_gemma":[0.99866533,0.0007021125,0.000121349105,0.00020999328,0.0001048645,0.00019635302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009800858,0.00015585935,0.00050447177,0.00010265006,0.00014679546,0.0000073848573,0.00008467703,0.000030930172,0.00002853884],"category_scores_gemma":[0.0018748798,0.00014865339,0.00013264161,0.00021751027,0.00012953924,0.00010264403,0.00029294856,0.0003105057,4.890152e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038431658,0.0006494774,0.9938263,0.00021033686,0.00007392331,0.00008632406,0.00095515134,0.000027608141,0.00015812248,0.000015330077,0.00030345426,0.003309623],"study_design_scores_gemma":[0.003592937,0.00024082857,0.98981416,0.00028583567,0.00031300052,0.00006738643,0.0052258344,0.00006584685,0.00007082156,0.00011225089,0.00008144762,0.00012967688],"about_ca_topic_score_codex":0.0002861055,"about_ca_topic_score_gemma":0.000017280374,"teacher_disagreement_score":0.004270683,"about_ca_system_score_codex":0.00027546997,"about_ca_system_score_gemma":0.0002954753,"threshold_uncertainty_score":0.6061909},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W4281710423","doi":"10.1186/s12880-022-00825-2","title":"Practical utility of liver segmentation methods in clinical surgeries and interventions","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Medicine; Radiology; Segmentation; Magnetic resonance imaging; Liver transplantation; Hepatocellular carcinoma; Radiological weapon; Surgical planning; Radiation treatment planning; Medical physics; Psychological intervention; Radiation therapy; Computer science; Transplantation; Artificial intelligence; Surgery","score_opus":0.32734356135223847,"score_gpt":0.4977588032630776,"score_spread":0.17041524191083912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281710423","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93022656,0.0020040874,0.06445648,0.0022686974,0.00018348977,0.00035912293,0.000005736976,0.000027227105,0.00046860275],"genre_scores_gemma":[0.92995256,0.00012412627,0.069585085,0.00017199646,0.000029913874,0.000052152558,0.000029111505,0.000007061683,0.000047980604],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99761075,0.0011570007,0.00056705857,0.00020138049,0.00033493448,0.00012885846],"domain_scores_gemma":[0.9980931,0.0015028993,0.00009693277,0.00012056938,0.000030583342,0.00015588035],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003809085,0.000070350565,0.0002827767,0.00009652382,0.00005206566,0.000006373351,0.0000348794,0.000032449276,0.0024828536],"category_scores_gemma":[0.0023003316,0.00006300934,0.00016686476,0.00015822977,0.00019443456,0.000072928844,0.00014818227,0.00030117002,0.0000017507213],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010118251,0.00044623186,0.8946337,0.00019477362,0.000022890677,0.00017578564,0.00013353516,1.6566473e-8,0.000055484543,0.00008706215,0.00041732233,0.10373198],"study_design_scores_gemma":[0.0019452117,0.00015212304,0.94651455,0.00017773978,0.0002080747,0.0002492379,0.0010458775,0.04758772,0.0006197541,0.000129548,0.0012794345,0.00009072064],"about_ca_topic_score_codex":0.00010737985,"about_ca_topic_score_gemma":0.00003218118,"teacher_disagreement_score":0.103641264,"about_ca_system_score_codex":0.000026215226,"about_ca_system_score_gemma":0.00024608,"threshold_uncertainty_score":0.998429},"labels":[],"label_agreement":null},{"id":"W4282923421","doi":"10.1186/s12880-022-00837-y","title":"Development and application of an electronic synoptic report for reporting and management of low-dose computed tomography lung cancer screening examination","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Health Sciences Centre; Foothills Medical Centre; Alberta Health Services; University of Alberta; McGill University Health Centre; University of Calgary","funders":"Fonds de Recherche du Québec - Santé; Alberta Cancer Foundation","keywords":"Lung cancer screening; Medicine; Audit; Lung cancer; Medical physics; Documentation; Radiology; Protocol (science); Cancer; Clinical audit; Computer science; Computed tomography; Pathology; Internal medicine","score_opus":0.013686559659945051,"score_gpt":0.3245052472482381,"score_spread":0.31081868758829306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282923421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9014693,0.0023638846,0.09497187,0.00023168343,0.00003793177,0.0008564023,0.0000020596367,0.00002390308,0.000042969274],"genre_scores_gemma":[0.9670373,0.00009724001,0.03194174,0.000056967663,0.000024373587,0.00070243777,0.000117234405,0.000012746902,0.000009909924],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99840444,0.000029041215,0.0006748081,0.00029572402,0.00043171202,0.00016429934],"domain_scores_gemma":[0.99899524,0.00007470928,0.0006139402,0.00014703807,0.00007172732,0.00009734454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012141227,0.00008801577,0.00023910523,0.000109237735,0.00010182078,0.000005911329,0.00003998919,0.000016158372,0.000009488198],"category_scores_gemma":[0.000054460426,0.00008272624,0.000034031746,0.00016618914,0.00004663536,0.000046274676,0.00008185466,0.00007172022,1.0211564e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010289768,0.00037396236,0.4806548,0.004028575,0.0003483934,0.00012278628,0.00049457804,0.00010363543,0.00056477194,0.00050252496,0.000026400627,0.51267666],"study_design_scores_gemma":[0.0032740855,0.00012134604,0.7952964,0.0013094774,0.00048608484,0.00031201114,0.00078952644,0.19485518,0.0027923977,0.000030986128,0.00057786814,0.0001546499],"about_ca_topic_score_codex":0.000068633955,"about_ca_topic_score_gemma":0.000028778515,"teacher_disagreement_score":0.51252204,"about_ca_system_score_codex":0.0001510565,"about_ca_system_score_gemma":0.0001766364,"threshold_uncertainty_score":0.3373478},"labels":[],"label_agreement":null},{"id":"W4290723383","doi":"10.1186/s12880-022-00869-4","title":"Correction: Practical utility of liver segmentation methods in clinical surgeries and interventions","year":2022,"lang":"en","type":"erratum","venue":"BMC Medical Imaging","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Qatar National Library","keywords":"Computer science; Segmentation; Medical physics; Psychological intervention; Information retrieval; Data science; Radiology; General surgery; Artificial intelligence; Medicine","score_opus":0.24066890194800242,"score_gpt":0.4755266812904354,"score_spread":0.234857779342433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290723383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07312527,0.17027192,0.35556275,0.01980481,0.25743252,0.009438852,0.00025037184,0.00082579465,0.11328771],"genre_scores_gemma":[0.4346437,0.045321908,0.32909322,0.0027682686,0.007439167,0.0016332424,0.009021842,0.0004683934,0.16961026],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955136,0.0018861742,0.0012199825,0.00048440602,0.0006650451,0.00023079717],"domain_scores_gemma":[0.9961016,0.0028960647,0.00035035095,0.00027277897,0.00009395534,0.0002852594],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004488401,0.00021968206,0.000858073,0.00025463043,0.00007142428,0.000019959543,0.000072372066,0.00028592226,0.008065194],"category_scores_gemma":[0.0078026922,0.0002005225,0.00048395406,0.00027714437,0.00040110937,0.00011025042,0.000218913,0.0016155661,0.0000045812662],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011841401,0.00048421763,0.62637717,0.0010280793,0.00008193311,0.0003774067,0.00010300306,5.754591e-9,0.0000018188753,0.000009781041,0.25601706,0.11540111],"study_design_scores_gemma":[0.0031648942,0.0004333621,0.70099074,0.004430617,0.0018957031,0.0010564929,0.0013724199,0.0836111,0.00010547676,0.00009061159,0.20236354,0.00048507043],"about_ca_topic_score_codex":0.00029020128,"about_ca_topic_score_gemma":0.00020257922,"teacher_disagreement_score":0.3615184,"about_ca_system_score_codex":0.00008373947,"about_ca_system_score_gemma":0.0012450103,"threshold_uncertainty_score":0.99284154},"labels":[],"label_agreement":null},{"id":"W4291019672","doi":"10.1186/s12880-022-00871-w","title":"Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Skin cancer; Deep learning; Artificial intelligence; Cancer; Artificial neural network; Suite; Machine learning; Process (computing); Audit; Architecture; Medicine","score_opus":0.022537303114234616,"score_gpt":0.30090372908324076,"score_spread":0.2783664259690061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291019672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35970262,0.00627601,0.62363607,0.0033541317,0.0046015065,0.0017387816,0.00007275559,0.00027343864,0.00034469916],"genre_scores_gemma":[0.99306095,0.00012656806,0.0028853805,0.0016903086,0.0011328697,0.0007252514,0.00004134236,0.00004357804,0.00029378087],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980786,0.0001322429,0.0003922568,0.00037050838,0.00064257404,0.00038381407],"domain_scores_gemma":[0.9989935,0.00030666502,0.00017168766,0.00022288255,0.00007521636,0.00023001252],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034406618,0.00016901658,0.00035311552,0.00010430527,0.0002585014,0.000019055271,0.00015592513,0.000042222033,0.0020475206],"category_scores_gemma":[0.00020511371,0.0001634802,0.00014894568,0.00027742464,0.0001001549,0.000052078878,0.00011325584,0.0002793214,0.0000026139028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028520632,0.00033727597,0.041210517,0.0005664381,0.00041785315,0.00024744595,0.00058376644,0.009627675,0.09127294,0.000040602223,0.024103777,0.82873964],"study_design_scores_gemma":[0.009239771,0.0005922444,0.028008886,0.0003951216,0.0012370212,0.0003592547,0.002491439,0.8745988,0.043978106,0.00071597024,0.03763923,0.0007441643],"about_ca_topic_score_codex":0.0024657832,"about_ca_topic_score_gemma":0.0017572477,"teacher_disagreement_score":0.8649711,"about_ca_system_score_codex":0.0002444512,"about_ca_system_score_gemma":0.00020968558,"threshold_uncertainty_score":0.99886477},"labels":[],"label_agreement":null},{"id":"W4294579759","doi":"10.1186/s12880-022-00886-3","title":"A head-to-head comparison of fast-SENC and feature tracking to LV long axis strain for assessment of myocardial deformation in chest pain patients","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"Universitätsklinikum Heidelberg; Deutsche Herzstiftung","keywords":"Feature tracking; Medicine; Ejection fraction; Cardiology; Internal medicine; Chest pain; Cardiac magnetic resonance imaging; Magnetic resonance imaging; Strain (injury); Nuclear medicine; Heart failure; Radiology; Artificial intelligence","score_opus":0.029548595855722747,"score_gpt":0.36856487542717437,"score_spread":0.33901627957145164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294579759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89341944,0.000177796,0.103039294,0.0019486173,0.00027037231,0.00088085677,0.00010469325,0.000021739907,0.00013719757],"genre_scores_gemma":[0.9888017,0.0000023520206,0.009953315,0.00087459054,0.000095449715,0.00009283365,0.00015570414,0.00001824111,0.0000057863085],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99795496,0.00020211481,0.00049944816,0.00023693877,0.0008307959,0.00027571103],"domain_scores_gemma":[0.9983722,0.0009526979,0.00012556097,0.0001566192,0.00012691692,0.0002660115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018105297,0.0001383236,0.00053481973,0.00025544892,0.000067631016,0.000014837788,0.00008337842,0.000042710068,0.000015737547],"category_scores_gemma":[0.003218747,0.00013251993,0.00011283491,0.0002974574,0.000054908407,0.00006548625,0.00011774494,0.00031786767,3.4548412e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101098834,0.00029666862,0.87202317,0.0004462553,0.000016007492,0.0000070445813,0.0017097187,0.0004302239,0.0003445707,0.000014446359,0.0008921567,0.12371862],"study_design_scores_gemma":[0.0027500156,0.0003167919,0.9481413,0.0005421638,0.00006411631,0.000012792777,0.0025239105,0.044957392,0.0001503942,0.000015284386,0.00040093667,0.00012490869],"about_ca_topic_score_codex":0.00013284596,"about_ca_topic_score_gemma":0.000107853775,"teacher_disagreement_score":0.12359371,"about_ca_system_score_codex":0.00015915062,"about_ca_system_score_gemma":0.00028834154,"threshold_uncertainty_score":0.54040056},"labels":[],"label_agreement":null},{"id":"W4296120535","doi":"10.1186/s12880-022-00893-4","title":"AbdomenNet: deep neural network for abdominal organ segmentation in epidemiologic imaging studies","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Medical Research Council; Bundesministerium für Bildung und Forschung","keywords":"Segmentation; Computer science; Artificial intelligence; Deep learning; Pipeline (software); Pattern recognition (psychology); Artificial neural network; Convolutional neural network; Population; Medicine","score_opus":0.06172572997421092,"score_gpt":0.39249230448359596,"score_spread":0.330766574509385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296120535","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62234384,0.07158367,0.13991328,0.15187652,0.007473403,0.0052913176,0.000041529893,0.00063079,0.0008456218],"genre_scores_gemma":[0.924691,0.001012507,0.045350265,0.024551272,0.001882235,0.0022288284,0.0001236091,0.000083238716,0.00007700132],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696106,0.00042810637,0.00071206776,0.00053515023,0.00065620994,0.0007073806],"domain_scores_gemma":[0.9955968,0.0036342,0.00019580142,0.00025042103,0.00007878412,0.00024395162],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032736128,0.00022502968,0.00060756225,0.00016683992,0.00028623905,0.000015403999,0.00022469893,0.00003263651,0.00097516977],"category_scores_gemma":[0.002529866,0.00020530216,0.00013668125,0.0004462059,0.00020597779,0.000118713215,0.0002775391,0.0004972249,0.0000073222595],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031780225,0.00013130251,0.85806257,0.00031869314,0.000051451716,0.00023374216,0.0006897275,0.003206453,0.00016283973,0.00010789195,0.04291559,0.09380193],"study_design_scores_gemma":[0.013008241,0.000333034,0.097389735,0.00070261594,0.00040101292,0.00088034745,0.018297592,0.8519135,0.00020765308,0.0053533926,0.0107279485,0.0007849022],"about_ca_topic_score_codex":0.00008231668,"about_ca_topic_score_gemma":0.00014487201,"teacher_disagreement_score":0.8487071,"about_ca_system_score_codex":0.00065589166,"about_ca_system_score_gemma":0.00021722817,"threshold_uncertainty_score":0.9999381},"labels":[],"label_agreement":null},{"id":"W4308531407","doi":"10.1186/s12880-022-00922-2","title":"Reproducibility and repeatability of a semi-automated pipeline to quantify trapeziometacarpal joint angles using dynamic computed tomography","year":2022,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Orthopedic Surgery and Rehabilitation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Arthritis Society; University of Calgary","keywords":"Reproducibility; Repeatability; Computer science; Computed tomography; Pipeline (software); Joint (building); Tomography; Medical physics; Radiology; Medicine; Nuclear medicine; Mathematics","score_opus":0.03484584910673516,"score_gpt":0.33063620316971504,"score_spread":0.2957903540629799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308531407","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9854172,0.0010748423,0.009777593,0.0022985598,0.0004499529,0.0006007964,0.00003200637,0.000303044,0.000046027064],"genre_scores_gemma":[0.99248964,0.000009234099,0.0067141764,0.00058013864,0.00007373761,0.000023262597,0.000056098797,0.000021879416,0.00003185005],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957412,0.0006974109,0.0009788441,0.0011619464,0.0010898325,0.00033077432],"domain_scores_gemma":[0.99714535,0.00096456194,0.00021213198,0.0010546416,0.00021055446,0.0004127573],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008683595,0.00018803234,0.0006800197,0.0004742184,0.00017056907,0.000011837416,0.000097839395,0.000063854124,0.00025120063],"category_scores_gemma":[0.008722059,0.00016579498,0.00022552288,0.0013306378,0.00028335548,0.0000848549,0.0002794178,0.00040634203,9.3013074e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033778927,0.0007071385,0.9561052,0.0009411536,0.00005663817,0.00005943162,0.0007812339,0.00024060221,0.020899849,0.000008009972,0.00023608396,0.019626927],"study_design_scores_gemma":[0.0012434442,0.00016756344,0.61155355,0.0002868182,0.00017252297,0.00038025898,0.0010032363,0.38320944,0.0010623862,0.000050389233,0.00065284705,0.00021752878],"about_ca_topic_score_codex":0.00016926519,"about_ca_topic_score_gemma":0.000017722185,"teacher_disagreement_score":0.38296884,"about_ca_system_score_codex":0.0001414738,"about_ca_system_score_gemma":0.00036571434,"threshold_uncertainty_score":0.9996279},"labels":[],"label_agreement":null},{"id":"W4313888688","doi":"10.1186/s12880-023-00963-1","title":"Establishment of local diagnostic reference levels for common adult CT examinations: a multicenter survey in Addis Ababa","year":2023,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiation Dose and Imaging","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Addis Ababa University","keywords":"Medicine; Computed tomography; Radiation dose; Nuclear medicine; Radiology; Radiological weapon; Radiation exposure; Percentile; Medical physics","score_opus":0.07000709195113021,"score_gpt":0.3586251215739661,"score_spread":0.2886180296228359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313888688","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95306873,0.00034349773,0.04205251,0.002425572,0.00031770268,0.0009640786,0.000162954,0.00011988968,0.00054506067],"genre_scores_gemma":[0.9975347,0.000118384865,0.00066529063,0.0009353399,0.00006579489,0.0001392097,0.00040399202,0.00002320981,0.00011406244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99801224,0.00017650542,0.00058247254,0.000290266,0.0005927494,0.00034579023],"domain_scores_gemma":[0.9936508,0.005582506,0.00011292067,0.00024062861,0.00019698718,0.00021615213],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0013860441,0.00013572679,0.00034310794,0.00032056306,0.00004406275,0.000023818235,0.00015931673,0.00003652809,0.00020399533],"category_scores_gemma":[0.017502528,0.0001222147,0.000061836254,0.00053079764,0.00014653112,0.0002097473,0.00007332289,0.00021522598,0.000038108054],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049423466,0.00020794256,0.86750823,0.00031795795,0.000017520124,0.00007552337,0.00030153868,0.00003087429,0.00008004453,0.000091882466,0.0060309535,0.12528813],"study_design_scores_gemma":[0.0029800255,0.00003054377,0.87927103,0.0010363939,0.00002890106,0.000011153924,0.00063089916,0.11446688,0.00091536116,0.000053284824,0.00045869194,0.00011681621],"about_ca_topic_score_codex":0.000992273,"about_ca_topic_score_gemma":0.0011680761,"teacher_disagreement_score":0.12517132,"about_ca_system_score_codex":0.00009964075,"about_ca_system_score_gemma":0.00030000118,"threshold_uncertainty_score":0.99077344},"labels":[],"label_agreement":null},{"id":"W4361271220","doi":"10.1186/s12880-023-00992-w","title":"Comparison of paraspinal muscle composition measurements using IDEAL fat–water and T2-weighted MR images","year":2023,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Concordia University","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec; Concordia University","keywords":"Multifidus muscle; Medicine; Magnetic resonance imaging; Limits of agreement; Nuclear medicine; Erector spinae muscles; Low back pain; Bland–Altman plot; Anatomy; Radiology; Lumbar; Pathology","score_opus":0.062452819346358594,"score_gpt":0.3831531838595345,"score_spread":0.3207003645131759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361271220","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869342,0.00027511973,0.0111086015,0.00090669404,0.0001716387,0.00025754812,0.0000017370858,0.00009010723,0.000254394],"genre_scores_gemma":[0.99578017,0.00001123904,0.0037930778,0.00014365884,0.00014264286,0.0000070597903,0.00008764622,0.000016703687,0.000017774011],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99807525,0.00017134949,0.0004378498,0.00023872113,0.0008104277,0.00026642918],"domain_scores_gemma":[0.9992851,0.00016832637,0.00007225122,0.00014048006,0.00011490449,0.00021890718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008521284,0.00012535881,0.00033326005,0.00017307565,0.00009648138,0.000015481268,0.00005643601,0.00006349348,0.00011827647],"category_scores_gemma":[0.00020255629,0.00008956598,0.00008204984,0.00018082352,0.0002598686,0.000097426215,0.000059349044,0.00016853695,0.000018190105],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007375103,0.00015468465,0.43719664,0.0013539966,0.000029605453,0.000009264582,0.00041322655,0.000015655216,0.55107456,0.000019476814,0.00054099393,0.00911813],"study_design_scores_gemma":[0.0023421848,0.00011307237,0.8249092,0.0013630824,0.000105393425,0.000015827616,0.00079644116,0.13583133,0.03410277,0.00016183309,0.000081474456,0.000177363],"about_ca_topic_score_codex":0.00007109245,"about_ca_topic_score_gemma":0.0000047120006,"teacher_disagreement_score":0.5169718,"about_ca_system_score_codex":0.000042419517,"about_ca_system_score_gemma":0.000059476675,"threshold_uncertainty_score":0.36523947},"labels":[],"label_agreement":null},{"id":"W4365451974","doi":"10.1186/s12880-023-01012-7","title":"The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma","year":2023,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Trauma Management and Diagnosis","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton; St Joseph's Health Care","funders":"Shaanxi University of Chinese Medicine","keywords":"Medicine; CAD; Radiology; Diagnostic accuracy; Confidence interval; Predictive value; Blunt; Computer-aided diagnosis; Internal medicine","score_opus":0.00858641042244376,"score_gpt":0.2665892589088851,"score_spread":0.25800284848644134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365451974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9929416,0.0005877156,0.004718598,0.0006475968,0.00028903477,0.0005724029,0.000001343206,0.000111443806,0.00013027283],"genre_scores_gemma":[0.9989211,0.00020352771,0.00040778384,0.00018734373,0.00010931263,0.00010756885,0.000022828855,0.00002940642,0.000011107511],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99742615,0.00021193571,0.00077555544,0.00031269348,0.000802636,0.00047101357],"domain_scores_gemma":[0.98687977,0.012409261,0.0002129446,0.0002923715,0.000056787972,0.00014884434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014787687,0.00019879595,0.0005067289,0.00040451213,0.00007492343,0.000023112998,0.00028317308,0.000084117644,0.00003714958],"category_scores_gemma":[0.005753946,0.00014335883,0.00011967507,0.0006936192,0.00021133074,0.000100295205,0.00013284652,0.00047075585,0.000022126458],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011091106,0.00013821402,0.8866195,0.0017351797,0.000033226486,0.00033345714,0.00038040392,0.013678462,0.000034978646,0.00008429826,0.000114006805,0.09673736],"study_design_scores_gemma":[0.0018080724,0.00008358014,0.5404031,0.0018408615,0.00006911856,0.0000056201384,0.00032448364,0.45493624,0.00041561306,0.0000045459046,0.000029918587,0.00007881929],"about_ca_topic_score_codex":0.0003767768,"about_ca_topic_score_gemma":0.00021357107,"teacher_disagreement_score":0.44125777,"about_ca_system_score_codex":0.00009286095,"about_ca_system_score_gemma":0.00013069846,"threshold_uncertainty_score":0.68884265},"labels":[],"label_agreement":null},{"id":"W4386810927","doi":"10.1186/s12880-023-01048-9","title":"Unsupervised registration of 3D knee implant components to biplanar X-ray images","year":2023,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Université de Montréal; Centre Hospitalier de l’Université de Montréal; Centrale des Syndicats du Québec","funders":"","keywords":"Radiography; Fiducial marker; Similarity (geometry); Computer science; Artificial intelligence; Similarity measure; Computer vision; Object (grammar); Image registration; Medicine; Pattern recognition (psychology); Radiology; Image (mathematics)","score_opus":0.028390632247519865,"score_gpt":0.3011336471335554,"score_spread":0.27274301488603553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386810927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.982373,0.00011965052,0.0051294914,0.0093989605,0.00060147315,0.00045810867,0.00003242504,0.00034948185,0.0015373891],"genre_scores_gemma":[0.99246395,0.000027482134,0.0047471602,0.0013139836,0.00019776389,0.000018208957,0.00013982765,0.000034389963,0.0010572511],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976838,0.00007091808,0.00051673816,0.00031091453,0.0010284579,0.00038916562],"domain_scores_gemma":[0.9985821,0.00042693867,0.00008642766,0.00035835578,0.0000772196,0.00046891763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005825686,0.00016878254,0.0004024847,0.00028430685,0.000060451945,0.000017174129,0.00013905017,0.00005995698,0.00035102403],"category_scores_gemma":[0.0015221098,0.00014129562,0.00009292097,0.0004607591,0.00014262562,0.000103115715,0.000114448005,0.00018322821,0.000509693],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047003667,0.0003131323,0.7302671,0.0010966912,0.00012537974,0.0015833039,0.0009458839,0.00010342934,0.10805267,0.00020352419,0.11177552,0.045063324],"study_design_scores_gemma":[0.004332914,0.00017585998,0.9671789,0.0010246612,0.00009104169,0.00044090624,0.0006372056,0.013072607,0.0027892676,0.000042415257,0.00991513,0.00029911572],"about_ca_topic_score_codex":0.00018542359,"about_ca_topic_score_gemma":0.000047591533,"teacher_disagreement_score":0.23691177,"about_ca_system_score_codex":0.000039075218,"about_ca_system_score_gemma":0.00021191424,"threshold_uncertainty_score":0.65512425},"labels":[],"label_agreement":null},{"id":"W4389429244","doi":"10.1186/s12880-023-01160-w","title":"MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network","year":2023,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Hôtel-Dieu de Québec","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Medical imaging; Discriminator; Generator (circuit theory); Segmentation; Deep learning; Magnetic resonance imaging; Similarity (geometry); Computer vision; Image (mathematics); Radiology; Medicine","score_opus":0.017761544924166747,"score_gpt":0.29587620855559194,"score_spread":0.2781146636314252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389429244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06633003,0.00030713482,0.92597395,0.00027084147,0.0017875627,0.0003935059,0.000008560879,0.0038957682,0.0010326566],"genre_scores_gemma":[0.3345112,0.0011107218,0.65285975,0.001960427,0.008253028,0.00017119193,0.0004812549,0.0005478463,0.00010459116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99543613,0.0003039702,0.00072921236,0.00062303187,0.0018836985,0.0010239417],"domain_scores_gemma":[0.9976567,0.00054570666,0.000072330535,0.00053784554,0.00013416797,0.0010532644],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014049595,0.00044842748,0.00051215815,0.00025974528,0.00037184556,0.00008473822,0.00071158336,0.00031717456,0.0041460963],"category_scores_gemma":[0.0014973478,0.0004253038,0.00015239265,0.0009175769,0.0003283654,0.00071044354,0.00054429466,0.0008641806,0.00019462545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025744227,0.00048947363,0.006191705,0.0006070447,0.00017640855,0.005548199,0.0032225815,0.10051815,0.2391327,0.0006448341,0.04629541,0.596916],"study_design_scores_gemma":[0.0011708055,0.000017785622,0.0004505554,0.0002770132,0.000024264627,0.00008152151,0.00026926072,0.9904921,0.0051631555,0.00060874893,0.00095235766,0.0004924401],"about_ca_topic_score_codex":0.0000656442,"about_ca_topic_score_gemma":0.00013479765,"teacher_disagreement_score":0.88997394,"about_ca_system_score_codex":0.00016340916,"about_ca_system_score_gemma":0.00030471865,"threshold_uncertainty_score":0.9998199},"labels":[],"label_agreement":null},{"id":"W4392241451","doi":"10.1186/s12880-024-01227-2","title":"PulmoNet: a novel deep learning based pulmonary diseases detection model","year":2024,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pneumonia; Convolutional neural network; Viral pneumonia; Medicine; Deep learning; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Pulmonary tuberculosis; Limiting; Computer science; Machine learning; Pathology; Tuberculosis; Disease; Internal medicine; Infectious disease (medical specialty)","score_opus":0.023630974082130156,"score_gpt":0.3155885968440308,"score_spread":0.2919576227619006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392241451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016254222,0.004705596,0.95517135,0.021593504,0.0005181474,0.0002959681,0.000005821281,0.0011923086,0.00026310387],"genre_scores_gemma":[0.98387665,0.000056625475,0.0029208998,0.012072272,0.000619981,0.00008376194,0.000042722735,0.000085777705,0.00024129545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755114,0.00007208018,0.0003429207,0.0006064535,0.0009913194,0.0004360626],"domain_scores_gemma":[0.9979271,0.0010953683,0.00004403285,0.00028059885,0.00006893987,0.0005839328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046147621,0.00023068543,0.00029125114,0.00032749615,0.00014705876,0.0001005959,0.00013365876,0.00010282142,0.00033025042],"category_scores_gemma":[0.0029251594,0.0002090503,0.00021028065,0.00044734974,0.0001501364,0.00018316318,0.00009157318,0.00067567604,0.000097947675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045071042,0.0015918426,0.05446792,0.005741531,0.00013745921,0.0038830596,0.00041988978,0.04606774,0.033940554,0.00013294532,0.0045437827,0.84862256],"study_design_scores_gemma":[0.0006039688,0.00002420833,0.0043888623,0.0015634495,0.00022121218,0.00019240484,0.000053259362,0.9811208,0.00048302702,0.000081091915,0.011057439,0.000210273],"about_ca_topic_score_codex":0.000108018794,"about_ca_topic_score_gemma":0.000022024165,"teacher_disagreement_score":0.96762246,"about_ca_system_score_codex":0.0002690944,"about_ca_system_score_gemma":0.00090997916,"threshold_uncertainty_score":0.8524824},"labels":[],"label_agreement":null},{"id":"W4394761328","doi":"10.1186/s12880-024-01253-0","title":"A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound","year":2024,"lang":"en","type":"review","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Machine learning; Generalization; Transfer of learning; Feature (linguistics); Supervised learning; Segmentation; Task (project management); Ultrasound; Pattern recognition (psychology); Medicine; Artificial neural network; Radiology","score_opus":0.0366002777191312,"score_gpt":0.3929904576735468,"score_spread":0.3563901799544156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394761328","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034727247,0.9911273,0.0024967685,0.0003292411,0.0005443153,0.0016697411,0.00011309026,0.00006304018,0.00018374919],"genre_scores_gemma":[0.016637815,0.9801178,0.0022025788,0.00017348015,0.00036235133,0.00013468764,0.00018309084,0.0001542457,0.00003394527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942854,0.0009173732,0.0018295229,0.0007264674,0.0015932452,0.00064800336],"domain_scores_gemma":[0.977143,0.02083389,0.0004919795,0.0006146312,0.00014336032,0.00077312015],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0071962876,0.0005916638,0.0030021504,0.00041743714,0.000060907685,0.00004289517,0.0006857708,0.0003155879,0.0002096304],"category_scores_gemma":[0.088643014,0.0003345038,0.00093803945,0.000914077,0.0007880829,0.00005993268,0.00033188378,0.0023244603,0.000002873456],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002556068,0.00029200225,0.070333175,0.05908451,0.0005963966,0.00027131132,0.00040377575,0.000004429562,0.0000015139142,0.000040384923,0.0029970652,0.86594987],"study_design_scores_gemma":[0.008190042,0.00041971143,0.052403115,0.42361903,0.007362184,0.005795558,0.00021343528,0.45687827,0.0000013609297,0.00021811912,0.043420117,0.0014790447],"about_ca_topic_score_codex":0.001226043,"about_ca_topic_score_gemma":0.000094909534,"teacher_disagreement_score":0.86447084,"about_ca_system_score_codex":0.0001856077,"about_ca_system_score_gemma":0.0055041253,"threshold_uncertainty_score":0.99997723},"labels":[],"label_agreement":null},{"id":"W4399511621","doi":"10.1186/s12880-024-01324-2","title":"Alterations in structural integrity of superior longitudinal fasciculus III associated with cognitive performance in cerebral small vessel disease","year":2024,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Anhui University; Anhui University of Science and Technology; National Natural Science Foundation of China","keywords":"Cognition; Structural integrity; Neuroscience; Disease; Computer science; Medicine; Pathology; Psychology","score_opus":0.052871482746483706,"score_gpt":0.34992412560570135,"score_spread":0.2970526428592176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399511621","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9752018,0.00027652763,0.02262869,0.0011813671,0.000041725467,0.00037190478,0.000022749224,0.00011895437,0.00015629368],"genre_scores_gemma":[0.9976585,0.000036102785,0.0018589699,0.00016892212,0.00004748082,0.00008667898,0.00008479818,0.000019087991,0.000039440994],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987691,0.000045425222,0.000322843,0.00032473452,0.00029696958,0.00024089377],"domain_scores_gemma":[0.99929655,0.00026191983,0.000040728853,0.00013665992,0.0000803062,0.00018381033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021188054,0.00013782119,0.0002447276,0.00014333044,0.00004357189,0.000023123626,0.00009230172,0.000036122037,0.00006275527],"category_scores_gemma":[0.0005877525,0.000106773005,0.000046338577,0.0004913229,0.00025000772,0.0001705478,0.000056550172,0.00060996175,0.000001722762],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014203378,0.00011839487,0.9907269,0.00026078848,0.0000107748965,0.00023725699,0.00023567541,0.000013485942,0.00016318128,0.00066927425,0.000017871245,0.0074043814],"study_design_scores_gemma":[0.0010926746,0.000036007903,0.79803425,0.0041008005,0.00004760646,0.000055508528,0.00013985812,0.1957465,0.000402709,0.0002148975,0.00001194865,0.0001172208],"about_ca_topic_score_codex":0.00008456426,"about_ca_topic_score_gemma":0.00021729703,"teacher_disagreement_score":0.19573301,"about_ca_system_score_codex":0.000096946125,"about_ca_system_score_gemma":0.00047646058,"threshold_uncertainty_score":0.43540767},"labels":[],"label_agreement":null},{"id":"W4402906571","doi":"10.1186/s12880-024-01431-0","title":"Disparities in the diagnostic efficacy of radiomics models in predicting various degrees of cognitive impairment in patients with cerebral small vessel disease","year":2024,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Cerebrovascular and genetic disorders","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Radiomics; Cognitive impairment; Disease; Medicine; Cognition; Magnetic resonance imaging; Intensive care medicine; Computer science; Pathology; Radiology; Psychiatry","score_opus":0.010148523905877687,"score_gpt":0.24322669029480948,"score_spread":0.23307816638893178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402906571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99265176,0.0021991746,0.0038955787,0.00027021143,0.000062335595,0.0007806474,0.000016228785,0.000014214153,0.00010986385],"genre_scores_gemma":[0.9994016,0.00009887634,0.00022867744,0.00011538063,0.000024892941,0.00005004257,0.00005780343,0.000019742081,0.0000029816858],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998267,0.00014074879,0.00044959038,0.00026177982,0.0006059228,0.00027499255],"domain_scores_gemma":[0.9976068,0.002003602,0.000048953607,0.0001688377,0.000040577936,0.00013120289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005525327,0.00014670934,0.0003220371,0.0001897667,0.000013748937,0.000015145547,0.00013063174,0.000038924423,0.000019277382],"category_scores_gemma":[0.0018933781,0.000099334546,0.000082093575,0.0003550893,0.00023431439,0.00006723998,0.00006162643,0.00028729226,5.3392966e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003709784,0.0010422992,0.98530734,0.0008287535,0.00003783364,0.00008885138,0.0028374393,0.0019357967,2.6856762e-7,0.00009501981,0.000012402917,0.007443026],"study_design_scores_gemma":[0.0060173934,0.000095879965,0.86969334,0.0051601,0.0001238819,0.0000051693914,0.0010168002,0.11763134,0.000002730592,0.00016622581,7.346429e-7,0.000086389664],"about_ca_topic_score_codex":0.0011833943,"about_ca_topic_score_gemma":0.00095707655,"teacher_disagreement_score":0.115695536,"about_ca_system_score_codex":0.000048253616,"about_ca_system_score_gemma":0.00046709098,"threshold_uncertainty_score":0.4050745},"labels":[],"label_agreement":null},{"id":"W4403986250","doi":"10.1186/s12880-024-01471-6","title":"Imaging and clinical manifestations of hematogenous dissemination in melioidosis","year":2024,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Burkholderia infections and melioidosis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Melioidosis; Computer science; Radiology; Medicine; Medical physics; Pathology","score_opus":0.020672755193779955,"score_gpt":0.3784496848238708,"score_spread":0.35777692963009083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403986250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9608052,0.0055726343,0.02484648,0.003672598,0.0007800846,0.00032490958,0.000006426301,0.00014405139,0.003847669],"genre_scores_gemma":[0.99621695,0.0009930875,0.002286001,0.00017557216,0.000187407,0.00002254925,0.0000144647165,0.000021630232,0.00008234995],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985815,0.000069897666,0.0005592001,0.0002756086,0.00032828725,0.00018546774],"domain_scores_gemma":[0.99869335,0.00084294163,0.000046711124,0.00015152388,0.00006418845,0.00020128734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009743111,0.000105635,0.00025917147,0.00033503916,0.000040698622,0.000031825923,0.000047461544,0.00005705295,0.00025136498],"category_scores_gemma":[0.0011522856,0.00009143456,0.00009768866,0.00038643408,0.00017452383,0.00014186972,0.000049174087,0.00027541217,0.000010517489],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019742904,0.0002963377,0.7636856,0.0009181915,0.000036703193,0.00061614346,0.00042793728,0.0000010414295,0.0016689164,0.0013275654,0.001927851,0.22907399],"study_design_scores_gemma":[0.0025999274,0.00010611522,0.77249575,0.009445001,0.0006182947,0.0058921385,0.0036371283,0.19711168,0.0020222166,0.00077734113,0.004851081,0.00044329648],"about_ca_topic_score_codex":0.00012349548,"about_ca_topic_score_gemma":0.00006189921,"teacher_disagreement_score":0.22863069,"about_ca_system_score_codex":0.000039917242,"about_ca_system_score_gemma":0.00025165212,"threshold_uncertainty_score":0.3728593},"labels":[],"label_agreement":null},{"id":"W4404860377","doi":"10.1186/s12880-024-01495-y","title":"Using ultrafast angio planewave ultrasensitive and conventional doppler imaging techniques to assess intramuscular blood perfusion in older adults","year":2024,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Cardiovascular and exercise physiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia","funders":"Hong Kong Polytechnic University","keywords":"Medicine; Blood flow; Vascularity; Ultrasound; Perfusion; Perfusion scanning; Nuclear medicine; Color doppler; Biceps; Ultrasonography; Radiology; Anatomy","score_opus":0.016437426850835955,"score_gpt":0.3036846873432458,"score_spread":0.28724726049240984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404860377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9637633,0.0019303738,0.032188315,0.00076374545,0.00026788737,0.00055391004,0.000013723802,0.000175977,0.0003427949],"genre_scores_gemma":[0.99367243,0.00015638114,0.0045850095,0.0008862226,0.0005166266,0.000027025579,0.000106148575,0.000039669456,0.000010495318],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9979582,0.0001334159,0.0003420999,0.000595073,0.00057251076,0.0003987232],"domain_scores_gemma":[0.9990654,0.0002624779,0.00002479259,0.00019897646,0.000086683365,0.00036164923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007085557,0.0002255983,0.00048600216,0.00034408763,0.00006148332,0.000057718484,0.00006687169,0.00009098524,0.00018816278],"category_scores_gemma":[0.00033002777,0.00019716915,0.00022243081,0.00028259555,0.00016736668,0.00013141107,0.00009808063,0.0004174904,0.000012283074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002296868,0.0014023996,0.08780171,0.0051459637,0.0007699138,0.025885424,0.0068027605,0.000077597724,0.51348084,0.00066810823,0.0031593507,0.35250905],"study_design_scores_gemma":[0.014532312,0.00021200678,0.39173222,0.047264095,0.0025037816,0.060011502,0.008096005,0.24313317,0.22804952,0.00041376014,0.0016399309,0.002411704],"about_ca_topic_score_codex":0.0003391047,"about_ca_topic_score_gemma":0.00003444454,"teacher_disagreement_score":0.35009736,"about_ca_system_score_codex":0.000074711694,"about_ca_system_score_gemma":0.00026087242,"threshold_uncertainty_score":0.8040325},"labels":[],"label_agreement":null},{"id":"W4405511023","doi":"10.1186/s12880-024-01523-x","title":"Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images","year":2024,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Vector Institute; Canada Research Chairs; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Ground truth; Computer science; Artificial intelligence; Segmentation; Cluster analysis; Pattern recognition (psychology); Pipeline (software); Classifier (UML); Sørensen–Dice coefficient; Deep learning; Image segmentation","score_opus":0.023173084489576164,"score_gpt":0.30729913843479456,"score_spread":0.2841260539452184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405511023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042125195,0.0007619267,0.95325714,0.0033403009,0.00013275305,0.00027193298,0.0000015935101,0.000081075836,0.000028089447],"genre_scores_gemma":[0.7218254,0.00004844558,0.27733895,0.00046212593,0.00014999682,0.00012181636,0.000016184369,0.000013399862,0.000023619923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989596,0.000049703973,0.00027874872,0.00032166048,0.00021621598,0.00017404603],"domain_scores_gemma":[0.9991382,0.000573291,0.000028553439,0.00015150472,0.00002792746,0.000080557664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036757757,0.00008707904,0.0001146774,0.00011261632,0.00005180447,0.00008351082,0.00021136181,0.000023722861,0.000007008602],"category_scores_gemma":[0.00018961867,0.00008345076,0.00002850134,0.0002798726,0.000060716047,0.0005292254,0.00012426269,0.000087709945,0.0000016534144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009979355,0.000051839954,0.0044928743,0.00051927916,0.000008753112,0.00002254432,0.0015822827,0.0055722366,0.2701546,0.01149399,0.0011650121,0.7049266],"study_design_scores_gemma":[0.00025101987,0.000009224312,0.00086024526,0.000106270374,0.000002764546,0.00002104489,0.00006245861,0.9913126,0.0060598934,0.0011477774,0.00008409228,0.00008260692],"about_ca_topic_score_codex":0.000011739029,"about_ca_topic_score_gemma":0.00009072471,"teacher_disagreement_score":0.98574036,"about_ca_system_score_codex":0.000032748514,"about_ca_system_score_gemma":0.000074906944,"threshold_uncertainty_score":0.34030232},"labels":[],"label_agreement":null},{"id":"W4407577764","doi":"10.1186/s12880-025-01580-w","title":"A nomogram for diagnosis of BI-RADS 4 breast nodules based on three-dimensional volume ultrasound","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Breast Lesions and Carcinomas","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"BI-RADS; Nomogram; Ultrasound; Volume (thermodynamics); Radiology; Computer science; Medicine; Ultrasonography; Breast imaging; Breast ultrasound; Mammography; Breast cancer; Oncology; Cancer; Internal medicine","score_opus":0.012359405676998215,"score_gpt":0.27792731602160087,"score_spread":0.26556791034460264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407577764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.860639,0.0013212906,0.11123982,0.02255254,0.00079817115,0.0011450677,0.00016324752,0.00018645941,0.0019544489],"genre_scores_gemma":[0.9903895,0.000005132889,0.005981947,0.0029864418,0.00020648855,0.00014352487,0.000067019915,0.000023597619,0.00019634521],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99826854,0.000046351408,0.00037987527,0.00035849007,0.00061998836,0.00032675435],"domain_scores_gemma":[0.99794066,0.0011560008,0.000073681505,0.00036895013,0.00017516792,0.00028552825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004955891,0.00018266121,0.0003970838,0.00022753922,0.00009603325,0.00001557521,0.00015307887,0.00010085038,0.0006584805],"category_scores_gemma":[0.0011182171,0.00014415615,0.00025981377,0.00030332393,0.00026698032,0.00003794706,0.000047366655,0.00021541785,0.000013670862],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034932792,0.00076864916,0.91915727,0.00047730308,0.00004223072,0.000028833194,0.000009777476,0.000021085723,0.0003037963,0.0005351705,0.01204618,0.0662604],"study_design_scores_gemma":[0.0038147115,0.000109139306,0.8738734,0.0023472654,0.00021676612,0.00014747996,0.000040582912,0.11580405,0.0004493217,0.00022735761,0.0028035154,0.00016637168],"about_ca_topic_score_codex":0.00014792204,"about_ca_topic_score_gemma":0.000067065426,"teacher_disagreement_score":0.12975055,"about_ca_system_score_codex":0.000055900087,"about_ca_system_score_gemma":0.0007322078,"threshold_uncertainty_score":0.72099},"labels":[],"label_agreement":null},{"id":"W4408113998","doi":"10.1186/s12880-025-01600-9","title":"A short-term predictive model for disease progression in acute-on-chronic liver failure: integrating spectral CT extracellular liver volume and clinical characteristics","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Volume (thermodynamics); Liver failure; Term (time); Chronic liver disease; Medicine; Computer science; Disease; Pathology; Intensive care medicine; Radiology; Internal medicine; Cirrhosis","score_opus":0.013066668349389219,"score_gpt":0.3041758409023284,"score_spread":0.29110917255293917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408113998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21011965,0.0026516332,0.78598684,0.00013278906,0.00027059892,0.000543059,0.00002678921,0.00019552404,0.00007311945],"genre_scores_gemma":[0.9855313,0.00061049784,0.013200151,0.00008153481,0.0002471546,0.00013528945,0.000046322115,0.000045542532,0.00010224068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980533,0.000064602056,0.00060389994,0.00047034703,0.0003012002,0.0005066416],"domain_scores_gemma":[0.9989057,0.0004349045,0.000052972195,0.00023083053,0.00004291845,0.00033268516],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004445301,0.0002934349,0.0003803831,0.00013641361,0.00010634993,0.00007061897,0.00021316932,0.000075109754,0.00001995042],"category_scores_gemma":[0.0005220592,0.000269739,0.00012573593,0.00013731571,0.00021503674,0.00031616993,0.00010951548,0.0007365027,0.0000038008825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003043396,0.0002958329,0.36429876,0.0014695756,0.00012423417,0.0010322843,0.0005129026,0.003509618,0.00039214606,0.0007589743,0.00051426433,0.62678707],"study_design_scores_gemma":[0.00080218003,0.00003240425,0.030070368,0.0018599364,0.00010452464,0.00001434672,0.000074869466,0.9664266,0.000050771632,0.0002216118,0.00009250966,0.00024987845],"about_ca_topic_score_codex":0.0000013113757,"about_ca_topic_score_gemma":0.00002629423,"teacher_disagreement_score":0.962917,"about_ca_system_score_codex":0.00018798801,"about_ca_system_score_gemma":0.00023466138,"threshold_uncertainty_score":0.9999755},"labels":[],"label_agreement":null},{"id":"W4409703256","doi":"10.1186/s12880-025-01662-9","title":"Use and applicability of magnetic resonance elastography of the lumbar spine in adults: a scoping review","year":2025,"lang":"en","type":"review","venue":"BMC Medical Imaging","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Magnetic resonance imaging; Magnetic resonance elastography; Elastography; Radiology; Lumbar spine; Computer science; Lumbar; Medicine; Medical physics; Ultrasound; Surgery","score_opus":0.018557558820502806,"score_gpt":0.3228487586219976,"score_spread":0.30429119980149477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409703256","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001334084,0.9964174,0.00046655934,0.00017636026,0.00011942675,0.002498246,0.000022808988,0.00002676836,0.00013899218],"genre_scores_gemma":[0.000028867405,0.99642426,0.0028502485,0.00039492175,0.00004713149,0.00019006134,0.000015324085,0.000028618007,0.000020572728],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9960854,0.00043088273,0.0016100192,0.000630903,0.0008882659,0.0003545167],"domain_scores_gemma":[0.9973246,0.00096751883,0.00045052852,0.00093317,0.00013076804,0.00019345431],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014276879,0.0003934575,0.0026650664,0.00038765182,0.00003858779,0.000013074274,0.00043090686,0.00017430926,0.000052226696],"category_scores_gemma":[0.005433938,0.00025863093,0.0007499302,0.002003987,0.0009843156,0.00006795621,0.0002692607,0.0008528131,8.036355e-7],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066499697,0.00009043712,0.01285329,0.47590196,0.000013010843,0.0000016409006,0.000016226837,1.35576625e-8,7.9193865e-7,0.00001243474,0.00007072446,0.5110328],"study_design_scores_gemma":[0.0005483193,0.00003171794,0.0051584807,0.9156482,0.0007369085,0.000086415705,0.000017226715,0.000010058503,4.3495555e-7,0.000007832235,0.077583045,0.00017133747],"about_ca_topic_score_codex":0.00012467569,"about_ca_topic_score_gemma":0.000081565115,"teacher_disagreement_score":0.5108615,"about_ca_system_score_codex":0.000030791158,"about_ca_system_score_gemma":0.0011964211,"threshold_uncertainty_score":0.9999866},"labels":[],"label_agreement":null},{"id":"W4410443794","doi":"10.1186/s12880-025-01715-z","title":"A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Renal cell carcinoma treatment","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Nomogram; Radiomics; Risk stratification; Renal cell carcinoma; Medicine; Clear cell renal cell carcinoma; Radiology; Oncology; Internal medicine","score_opus":0.014802615946719501,"score_gpt":0.28158767438572313,"score_spread":0.26678505843900363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410443794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96776795,0.0021058626,0.026280176,0.0013772402,0.0001662757,0.0013019933,0.000025363259,0.000048389757,0.0009267375],"genre_scores_gemma":[0.98148507,0.00005165713,0.017357336,0.00054255006,0.00006835463,0.0001775447,0.00014455899,0.000025480027,0.00014742391],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977128,0.00025832703,0.00057257304,0.00063319,0.00038832123,0.00043481024],"domain_scores_gemma":[0.9985897,0.00046326336,0.0001372233,0.00033085354,0.00013272952,0.0003462083],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006824143,0.0002882357,0.00050397543,0.00025466265,0.00012727632,0.00007505319,0.00014634858,0.000073939256,0.00004342708],"category_scores_gemma":[0.00048285304,0.00025072272,0.00012351108,0.00034798696,0.00033077007,0.00009866276,0.000047389294,0.0004915066,0.0000045904158],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048058834,0.0018996387,0.90349776,0.002055822,0.000038791157,0.00108266,0.0010570774,0.00011012555,0.017413959,0.00055124727,0.0029812679,0.0645058],"study_design_scores_gemma":[0.031031244,0.0013205094,0.1800148,0.0011432164,0.0005192336,0.00020925756,0.0018777768,0.6649507,0.116249815,0.00037940172,0.0015397011,0.00076430495],"about_ca_topic_score_codex":0.0003369032,"about_ca_topic_score_gemma":0.00015107376,"teacher_disagreement_score":0.7234829,"about_ca_system_score_codex":0.00027466935,"about_ca_system_score_gemma":0.0013949827,"threshold_uncertainty_score":0.9999945},"labels":[],"label_agreement":null},{"id":"W4411897422","doi":"10.1186/s12880-025-01774-2","title":"Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of Tabriz; Tehran University of Medical Sciences and Health Services; Tabriz University of Medical Sciences","keywords":"Radiomics; Imaging phantom; Robustness (evolution); Bayesian probability; Lung cancer; Computer science; Artificial intelligence; Medical physics; Radiology; Medicine; Oncology","score_opus":0.008800969502012123,"score_gpt":0.31396031464978047,"score_spread":0.3051593451477683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411897422","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5025811,0.003988229,0.4894338,0.0024777302,0.0006729045,0.000523712,0.000001567332,0.0000702903,0.00025066387],"genre_scores_gemma":[0.88046026,0.0012006669,0.11366374,0.0036912002,0.0006234319,0.00016883381,0.000011947305,0.000059360067,0.000120535755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743724,0.0001219887,0.00070676947,0.00068241893,0.00050789706,0.000543711],"domain_scores_gemma":[0.99829257,0.00037460294,0.00018855366,0.0003224183,0.00022762018,0.00059421104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009000715,0.00030751934,0.00067424186,0.00034816778,0.00030782836,0.00007516325,0.00017085436,0.00007836034,0.00006121064],"category_scores_gemma":[0.0010226717,0.00027586197,0.00010466689,0.0005090362,0.00021891546,0.000117404954,0.00022667591,0.0005867669,0.0000017146725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003511267,0.0002953029,0.40859464,0.0058840318,0.00047279327,0.00026812125,0.0030962864,0.0027430546,0.047196932,0.000106449654,0.0020758272,0.5289154],"study_design_scores_gemma":[0.0030859113,0.000027645412,0.0021489202,0.004407212,0.0005068254,0.0005642944,0.0024343745,0.9816036,0.0042529977,0.00007179467,0.00053492043,0.0003614863],"about_ca_topic_score_codex":0.00028897435,"about_ca_topic_score_gemma":0.00009699574,"teacher_disagreement_score":0.97886056,"about_ca_system_score_codex":0.0003480843,"about_ca_system_score_gemma":0.00089388806,"threshold_uncertainty_score":0.99996936},"labels":[],"label_agreement":null},{"id":"W4412450116","doi":"10.1186/s12880-025-01823-w","title":"Non-invasive liver fibrosis screening on CT images using radiomics","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; Toronto General Hospital; Vector Institute; Princess Margaret Cancer Centre; Canada Research Chairs; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Radiomics; Logistic regression; Radiology; Receiver operating characteristic; Asymptomatic; Cohort; Neuroradiology; Biopsy; Ultrasound; Nuclear medicine; Artificial intelligence; Internal medicine; Computer science","score_opus":0.01644999647423392,"score_gpt":0.31342088793424555,"score_spread":0.29697089146001165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412450116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39792654,0.0020047934,0.5787196,0.0073409392,0.001156751,0.00058536546,0.000006640615,0.00032494008,0.011934401],"genre_scores_gemma":[0.88098043,0.00048444024,0.099066295,0.016864039,0.00102319,0.000023037635,0.00004426153,0.00010123101,0.0014130984],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99686533,0.0001304539,0.0005995127,0.00069165166,0.0009971019,0.0007159544],"domain_scores_gemma":[0.9973582,0.0012061575,0.00014788189,0.0005310489,0.00011909495,0.00063761283],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010706575,0.00037197585,0.00066656934,0.0004984058,0.00032692045,0.00011477605,0.00036348336,0.00009559651,0.00035001565],"category_scores_gemma":[0.004849942,0.00032482136,0.0003054083,0.0005471241,0.0004660742,0.00016365366,0.00023833492,0.0012053906,0.000044419266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052425766,0.00055633666,0.46181327,0.0020635643,0.00045010645,0.004294424,0.00034355218,0.0010306237,0.020137379,0.0012384095,0.049602553,0.45794553],"study_design_scores_gemma":[0.0062294775,0.00008696421,0.042737808,0.009198205,0.00053650746,0.0011705928,0.00041239877,0.91972685,0.00974191,0.0002655765,0.009227244,0.0006664713],"about_ca_topic_score_codex":0.00029168726,"about_ca_topic_score_gemma":0.0000059059425,"teacher_disagreement_score":0.9186962,"about_ca_system_score_codex":0.00019641519,"about_ca_system_score_gemma":0.0009752157,"threshold_uncertainty_score":0.99992037},"labels":[],"label_agreement":null},{"id":"W4412939140","doi":"10.1186/s12880-025-01855-2","title":"Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Vector Institute; Mental Health Research Canada; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Radiomics; Computer science; Protocol (science); Pipeline transport; Open source; Medical physics; Machine learning; Artificial intelligence; Medicine; Software; Operating system; Engineering; Pathology","score_opus":0.024068033846008134,"score_gpt":0.38666837058319325,"score_spread":0.3626003367371851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412939140","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077315657,0.0010095943,0.842224,0.018931493,0.00053955236,0.12512916,0.00011527655,0.00042404223,0.0038953002],"genre_scores_gemma":[0.0899587,0.000675541,0.7128764,0.008876371,0.0017349,0.17949955,0.0014766953,0.00039772628,0.00450415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969931,0.00021299713,0.0009434001,0.00086413464,0.0005642458,0.0004220956],"domain_scores_gemma":[0.997573,0.0009870678,0.0002867269,0.0005904686,0.00020481372,0.00035791157],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0043915403,0.0002750718,0.00090702286,0.00037951302,0.0002871158,0.000113093454,0.0004427164,0.00014581246,0.000092285416],"category_scores_gemma":[0.019591361,0.00023088425,0.00014153836,0.0005860855,0.0004555091,0.0001771212,0.0005438419,0.0008016531,9.151572e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015198753,0.0018356772,0.35559675,0.011692867,0.0005512407,0.00019333609,0.0003585888,0.00020100229,0.036286056,0.0044297613,0.21514148,0.3585145],"study_design_scores_gemma":[0.03673453,0.00039736592,0.004196064,0.0043274593,0.00042612763,0.0011256465,0.00015016025,0.64580244,0.004107646,0.0019048292,0.3003097,0.0005180433],"about_ca_topic_score_codex":0.00022722028,"about_ca_topic_score_gemma":0.000037286,"teacher_disagreement_score":0.64560145,"about_ca_system_score_codex":0.00013426386,"about_ca_system_score_gemma":0.0013347233,"threshold_uncertainty_score":0.988667},"labels":[],"label_agreement":null},{"id":"W4413400506","doi":"10.1186/s12880-025-01858-z","title":"Determination of lymph node metastasis using quantitative ultrasound elastography of papillary thyroid carcinoma nodule: a systematic review and meta-analysis","year":2025,"lang":"en","type":"review","venue":"BMC Medical Imaging","topic":"Thyroid Cancer Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Medicine; Meta-analysis; Elastography; Nodule (geology); Cochrane Library; Thyroid carcinoma; Lymph node metastasis; Ultrasound; Carcinoma; Radiology; Metastasis; Thyroid; Internal medicine; Cancer","score_opus":0.07026400690318765,"score_gpt":0.38415513952340014,"score_spread":0.3138911326202125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413400506","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017014954,0.98910946,0.008190467,0.00006454382,0.000039024257,0.0021804627,0.00032117183,0.000018090417,0.00005976132],"genre_scores_gemma":[0.00045519858,0.9879163,0.010697058,0.00024174739,0.000013834611,0.000461937,0.0001537622,0.000038913648,0.000021282156],"study_design_codex":"systematic_review","study_design_gemma":"meta_analysis","domain_scores_codex":[0.99458766,0.0010714912,0.0023063326,0.00067616464,0.0010724758,0.0002859045],"domain_scores_gemma":[0.9930477,0.0037398846,0.0017870058,0.00081617763,0.0003587603,0.00025048488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00149714,0.0006217449,0.012157329,0.0010737409,0.00005893446,0.000019247766,0.00023841903,0.00015235278,0.00016353151],"category_scores_gemma":[0.0025368347,0.00039524492,0.0042627477,0.0020412258,0.00028339957,0.000086486325,0.00011345787,0.00021816051,0.00000134171],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045282386,0.00016393545,0.00047076476,0.9204923,0.07530724,0.000037618614,0.000038371167,3.8642705e-7,7.179002e-7,0.000038316684,0.000022933948,0.0034228957],"study_design_scores_gemma":[0.00033534953,0.000032756077,0.00004966363,0.17076387,0.8281597,0.0001545482,0.00006488569,0.00010278449,0.0000051595734,0.000005714057,0.0001146904,0.00021088941],"about_ca_topic_score_codex":0.00017987184,"about_ca_topic_score_gemma":0.000019813517,"teacher_disagreement_score":0.75285244,"about_ca_system_score_codex":0.0001504199,"about_ca_system_score_gemma":0.00095590315,"threshold_uncertainty_score":0.9998499},"labels":[],"label_agreement":null},{"id":"W4414547297","doi":"10.1186/s12880-025-01914-8","title":"Efficacy of PSMA PET/CT radiomics analysis for risk stratification in newly diagnosed prostate cancer: a multicenter study","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Radiomics; Prostate cancer; Prostate; Logistic regression; Multicenter study; Random forest; Receiver operating characteristic; Prostate-specific antigen","score_opus":0.02435989158641213,"score_gpt":0.3832247515913676,"score_spread":0.3588648600049555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414547297","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99117255,0.0015298732,0.003521316,0.0010702126,0.00010829726,0.002458069,0.000036620324,0.00002729282,0.00007576697],"genre_scores_gemma":[0.9970857,0.0008263945,0.0010365972,0.00009801828,0.00004497036,0.00052921835,0.000094216906,0.000013126398,0.0002717706],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99817556,0.00015724575,0.00052461127,0.00038466297,0.00045229733,0.00030564258],"domain_scores_gemma":[0.9983545,0.0009039312,0.00013066643,0.0003038065,0.0001476773,0.00015942131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081886555,0.00014185403,0.00047508167,0.0004934443,0.00006281591,0.000023692126,0.00014050899,0.000012215701,0.00006443384],"category_scores_gemma":[0.0013406163,0.00010838979,0.00016573617,0.0009843671,0.00012752063,0.00006496623,0.000042701115,0.00022170431,0.0000013324691],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012704597,0.0013581644,0.961175,0.0002563756,0.00059749855,0.00016353374,0.00068242115,0.00014399253,0.00015619949,0.000008137356,0.00019240596,0.03399581],"study_design_scores_gemma":[0.02479172,0.00009911819,0.8812909,0.00036520712,0.0013663997,0.0000022767624,0.0019680085,0.0890056,0.0008758406,0.000025464244,0.00010919352,0.00010023383],"about_ca_topic_score_codex":0.001681399,"about_ca_topic_score_gemma":0.0019812547,"teacher_disagreement_score":0.08886161,"about_ca_system_score_codex":0.00021180662,"about_ca_system_score_gemma":0.00086752645,"threshold_uncertainty_score":0.44200072},"labels":[],"label_agreement":null},{"id":"W4415618229","doi":"10.1186/s12880-025-01979-5","title":"MRI study on hippocampal subfield volume loss and abnormal functional connectivity in patients with diabetic retinopathy","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Retinal Diseases and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Six Talent Peaks Project in Jiangsu Province; Government of Jiangsu Province","keywords":"Hippocampal formation; Neuroimaging; Functional connectivity; Hippocampus; Cognition; Functional neuroimaging; Functional imaging; Diabetic retinopathy; Central nervous system","score_opus":0.006697449533890313,"score_gpt":0.2532443151334516,"score_spread":0.24654686559956127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415618229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970503,0.00007803057,0.00050986896,0.0007007921,0.00015391182,0.0005266237,0.000004163561,0.00002901003,0.0009473208],"genre_scores_gemma":[0.99906445,0.0000039047336,0.000036968922,0.00064216944,0.00004515818,0.00003935754,0.000022719638,0.000010003562,0.00013527159],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985409,0.00010521193,0.00020113075,0.00034353396,0.00056592823,0.00024333698],"domain_scores_gemma":[0.9992529,0.00024416574,0.000037952785,0.00017568462,0.00005689268,0.00023241085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021932366,0.00015118906,0.00026136258,0.0001158251,0.00006654072,0.000020241045,0.00004573395,0.00004032086,0.00016411752],"category_scores_gemma":[0.00042407666,0.00010899957,0.000036712416,0.00016712409,0.00011822769,0.00006226772,0.000056596986,0.00023600002,0.000012215793],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013477746,0.0018689482,0.99201024,0.000076748634,0.00004118421,0.00023918082,0.00005739514,0.0000016499052,5.824634e-7,0.00003492383,0.00013509978,0.004186254],"study_design_scores_gemma":[0.008463741,0.0005715754,0.98935044,0.00040494482,0.00010924288,0.0000065466043,0.00020570809,0.0007135586,0.0000055751675,0.00005825196,0.00001867464,0.000091737056],"about_ca_topic_score_codex":0.0000651757,"about_ca_topic_score_gemma":0.000033708773,"teacher_disagreement_score":0.007115966,"about_ca_system_score_codex":0.000064790634,"about_ca_system_score_gemma":0.00019463216,"threshold_uncertainty_score":0.44448733},"labels":[],"label_agreement":null},{"id":"W4415849333","doi":"10.1186/s12880-025-01995-5","title":"Association of quantitative renal surface nodularity with the renal dysfunction progression in patients with arterial hypertension","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Renal and Vascular Pathologies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Soochow University","keywords":"Biomarker; Kidney; Imaging biomarker; Kidney disease; Renal artery; Blood pressure","score_opus":0.013105838476819875,"score_gpt":0.2758738547561285,"score_spread":0.2627680162793086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415849333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9850102,0.00008901075,0.0119067365,0.0023377337,0.00010549373,0.00035392668,0.0000017020512,0.000025717834,0.00016948604],"genre_scores_gemma":[0.9962564,0.000009362081,0.0033608777,0.00019750909,0.000034257955,0.000009397846,0.000034523793,0.0000070504934,0.000090629124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984054,0.00021875964,0.0002240924,0.00020764962,0.000775655,0.00016842509],"domain_scores_gemma":[0.99920523,0.00020564729,0.00014532445,0.00015115963,0.00024529052,0.000047318958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007635979,0.0001057936,0.00025859458,0.00006396109,0.00006376,0.000012785141,0.00006297451,0.000068800895,0.000016476743],"category_scores_gemma":[0.0009018654,0.000050514285,0.000042718213,0.00028165872,0.00013372458,0.00008707722,0.000042096905,0.00026056002,0.0000017611156],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024678633,0.00030493786,0.98851174,0.000090109876,0.00006769671,0.000025833873,0.00011895645,0.000039276954,0.0028213842,0.00004222001,0.00070502853,0.0048049684],"study_design_scores_gemma":[0.0059536453,0.00027536208,0.9875104,0.0015171015,0.00011560672,0.000007152224,0.00017329941,0.0029411935,0.0012807831,0.00002597424,0.0001299031,0.00006958571],"about_ca_topic_score_codex":0.000046434267,"about_ca_topic_score_gemma":0.00010722664,"teacher_disagreement_score":0.0112462,"about_ca_system_score_codex":0.00007480699,"about_ca_system_score_gemma":0.00022389699,"threshold_uncertainty_score":0.20599128},"labels":[],"label_agreement":null},{"id":"W4416225917","doi":"10.1186/s12880-025-02014-3","title":"Predicting mild cognitive impairment in patients with Parkinson’s disease by integrating striatal MRI radiomics with clinical features","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Radiomics; Generalizability theory; Disease; Cognitive impairment; Predictive value; Magnetic resonance imaging; Neuroimaging; Cognition","score_opus":0.00645340450959763,"score_gpt":0.30297135936234443,"score_spread":0.2965179548527468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416225917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96278834,0.00071968324,0.02982913,0.004310901,0.00033388284,0.0010396284,0.00003024249,0.00015152126,0.0007966925],"genre_scores_gemma":[0.9890832,0.00009738395,0.0056426777,0.0042180885,0.00028993268,0.00006776527,0.00032424266,0.00005962733,0.0002170819],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99609536,0.00032897267,0.00086351397,0.000830447,0.0011940054,0.00068768114],"domain_scores_gemma":[0.9969876,0.0013270495,0.0002394934,0.00032194782,0.00016722712,0.0009566993],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015330177,0.00039503328,0.00072552345,0.00026643433,0.00017228814,0.0000939171,0.00025840593,0.00012871614,0.00005929085],"category_scores_gemma":[0.0044316184,0.0002695405,0.00013631758,0.00049815193,0.00057042524,0.00017816422,0.00015544036,0.0018892258,0.0000031631214],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026132583,0.0007142512,0.9637244,0.00022444528,0.00012444386,0.00014440864,0.00015668936,0.00001531534,0.0000012349639,0.00003449709,0.0050446233,0.027202439],"study_design_scores_gemma":[0.01853485,0.000263682,0.92205536,0.0069384146,0.0003161178,0.000017067257,0.00076221087,0.049939115,0.0000110473,0.00002199627,0.0008301396,0.00030997928],"about_ca_topic_score_codex":0.00018743628,"about_ca_topic_score_gemma":0.00008125221,"teacher_disagreement_score":0.0499238,"about_ca_system_score_codex":0.00020786113,"about_ca_system_score_gemma":0.0012703789,"threshold_uncertainty_score":0.9999757},"labels":[],"label_agreement":null},{"id":"W4416717347","doi":"10.1186/s12880-025-02026-z","title":"Post-valvular energy exchange in bicuspid aortic valve with regurgitation and stenosis and its association with left ventricular workload: a 4D flow magnetic resonance imaging cross-sectional study","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Circle Cardiovascular Imaging; Calgary Health Foundation; Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Bicuspid aortic valve; Regurgitation (circulation); Stenosis; Magnetic resonance imaging; Cardiac magnetic resonance; Aortic valve; Flow (mathematics)","score_opus":0.006233513803970634,"score_gpt":0.2926355904024455,"score_spread":0.2864020765984749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416717347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9698289,0.028366575,0.00059438456,0.00042452628,0.000093053306,0.0005364469,0.00001235715,0.000043481,0.00010030037],"genre_scores_gemma":[0.9985318,0.00027264422,0.00026933136,0.00047925801,0.000073967945,0.000106278596,0.00004032239,0.000023104598,0.00020326163],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99785453,0.00014227442,0.00031607226,0.0005150212,0.00084403495,0.00032805887],"domain_scores_gemma":[0.99898875,0.000264157,0.00009901261,0.00015920696,0.00026848694,0.00022036325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004341998,0.00021186113,0.00033804218,0.00026047367,0.00015086796,0.00014365747,0.000046913898,0.00005534947,0.000047327212],"category_scores_gemma":[0.00052314636,0.00016604281,0.00012647301,0.00038295955,0.00008468234,0.00020343204,0.000063865824,0.00016733762,0.0000013403848],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035347903,0.00034837163,0.9805545,0.00012886511,0.00015359888,0.00024399761,0.0001593107,0.000013041472,0.000009971143,0.000015436739,0.000018100607,0.018001297],"study_design_scores_gemma":[0.007022767,0.00016740883,0.9830891,0.0008119995,0.0005671006,0.000060602266,0.0003499966,0.007694797,0.000014583744,0.000026875987,0.00004085276,0.00015392767],"about_ca_topic_score_codex":0.0003063814,"about_ca_topic_score_gemma":0.000110063425,"teacher_disagreement_score":0.028702956,"about_ca_system_score_codex":0.00027115457,"about_ca_system_score_gemma":0.00029793504,"threshold_uncertainty_score":0.6771029},"labels":[],"label_agreement":null},{"id":"W4416981998","doi":"10.1186/s12880-025-02095-0","title":"Hypothalamic subunit atrophy and glymphatic dysfunction in idiopathic hypogonadotropic hypogonadism: associations with cognitive impairment","year":2025,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Cerebrospinal fluid and hydrocephalus","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Glymphatic system; Atrophy; Precuneus; Cognition; Cognitive decline; Postcentral gyrus; Cingulum (brain); Corpus callosum","score_opus":0.013037055741647423,"score_gpt":0.25841056326883344,"score_spread":0.245373507527186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416981998","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9904254,0.000633888,0.005697049,0.0009962688,0.00024925818,0.00035215114,0.000009479173,0.000090238136,0.0015462701],"genre_scores_gemma":[0.9973185,0.00012756402,0.00007922401,0.0020817118,0.00007593674,0.000057847286,0.000007098557,0.000018607516,0.00023353015],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978236,0.00025091754,0.0003613755,0.0005298486,0.0005942605,0.000439981],"domain_scores_gemma":[0.9988014,0.0007262228,0.00008905679,0.00014453317,0.00004931444,0.00018949014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035658982,0.00020964297,0.00029689097,0.00029810925,0.00024072506,0.0000925229,0.00016556717,0.00007059664,0.00006133624],"category_scores_gemma":[0.001805882,0.00018257347,0.000049900842,0.00068063004,0.00029387992,0.00024007338,0.0001101794,0.00030022807,0.000028912895],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044491442,0.0008232501,0.9264977,0.0005360775,0.0000412042,0.00058005046,0.00084260065,0.000054276446,0.019695576,0.009719696,0.00021535569,0.04054927],"study_design_scores_gemma":[0.006187912,0.00017304943,0.9553234,0.0013222027,0.00011054576,0.00020273661,0.0004648772,0.028934425,0.003664525,0.003150281,0.000065159256,0.00040089327],"about_ca_topic_score_codex":0.0001768859,"about_ca_topic_score_gemma":0.00030007696,"teacher_disagreement_score":0.040148377,"about_ca_system_score_codex":0.00023491109,"about_ca_system_score_gemma":0.00054261944,"threshold_uncertainty_score":0.744513},"labels":[],"label_agreement":null}]}