{"meta":{"query_hash":"465036eb6370","filters":{"venue":"Journal of Medical Imaging"},"cohort_total":59,"direct_labels_cover":1,"predictions_cover":59,"exported":59,"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/465036eb6370","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Medical+Imaging"},"results":[{"id":"W1492342382","doi":"10.1117/1.jmi.2.3.034002","title":"Hole filling with oriented sticks in ultrasound volume reconstruction","year":2015,"lang":"en","type":"article","venue":"Journal of 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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Center for Research Resources; National Institute of Mental Health","keywords":"Interpolation (computer graphics); Medicine; Voxel; Artificial intelligence; Computer vision; Ultrasound; Volume (thermodynamics); Iterative reconstruction; Fidelity; Image (mathematics); Radiology; Computer science","score_opus":0.010760787639555748,"score_gpt":0.289932921175335,"score_spread":0.27917213353577924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492342382","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":[0.8771998,0.0009736273,0.09832117,0.020375494,0.0010920359,0.00010486552,5.569316e-7,0.000028996643,0.0019034032],"genre_scores_gemma":[0.98783964,0.00013123474,0.009810802,0.0012302708,0.00083094096,0.0000011644041,0.0000034936859,0.000026237976,0.00012619293],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742013,0.000092859416,0.000614808,0.00015398393,0.0014332976,0.00028488957],"domain_scores_gemma":[0.9983566,0.00016862761,0.00029219198,0.00012253094,0.00029987993,0.000760164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025835638,0.00012908151,0.00041148378,0.0003042184,0.000036699068,0.000038968523,0.0001497999,0.00007329939,0.00020631375],"category_scores_gemma":[0.0051468336,0.000089898225,0.00007040306,0.00031373397,0.00023318204,0.00023430317,0.000020754316,0.0014231405,0.0000074081518],"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":[0.00042092067,0.00024066135,0.60641724,0.00007176164,0.00009265012,0.0041368394,0.0011324261,0.00063311413,0.0008716676,0.00007246093,0.0077818967,0.37812835],"study_design_scores_gemma":[0.027248474,0.0007447288,0.055493437,0.008582376,0.00044212735,0.09657174,0.009696888,0.7304361,0.00040845753,0.0007205167,0.06902247,0.00063266023],"about_ca_topic_score_codex":0.0000705835,"about_ca_topic_score_gemma":0.0000042351353,"teacher_disagreement_score":0.729803,"about_ca_system_score_codex":0.00017410325,"about_ca_system_score_gemma":0.00061606074,"threshold_uncertainty_score":0.6182916},"labels":[],"label_agreement":null},{"id":"W1849982166","doi":"10.1117/1.jmi.3.1.011004","title":"Consistency of visual assessments of mammographic breast density from vendor-specific “for presentation” images","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Digital Radiography and Breast Imaging","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":"Nova Scotia Health Authority; Dalhousie University","funders":"","keywords":"Medicine; Mammography; Confidence interval; Presentation (obstetrics); Medical physics; Breast cancer; Consistency (knowledge bases); Visual inspection; Vendor; Breast imaging; Radiology; Nuclear medicine; Artificial intelligence; Cancer","score_opus":0.02717679340584257,"score_gpt":0.35254935707731755,"score_spread":0.325372563671475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1849982166","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":[0.96285504,0.003216859,0.027963921,0.0038800035,0.0007125717,0.00021346123,0.00007332558,0.000016057946,0.0010687776],"genre_scores_gemma":[0.9954919,0.00012024348,0.0038254578,0.00014504044,0.00036774596,0.0000015848126,0.000018088753,0.00001661153,0.000013303599],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970924,0.000071034934,0.000900948,0.00014931864,0.0015748775,0.00021142136],"domain_scores_gemma":[0.99739593,0.00030969313,0.0006778558,0.00014618202,0.0009775603,0.00049276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010680717,0.00014006431,0.0005951575,0.00040706128,0.000028047913,0.000031418436,0.00020924576,0.000054387987,0.000052904714],"category_scores_gemma":[0.0004082972,0.00011161722,0.0003789846,0.00032463443,0.00043708258,0.00040261302,0.000052240877,0.00029787136,8.522714e-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":[0.0012521987,0.0013636359,0.7040052,0.000250904,0.00061157765,0.00049522315,0.00028716165,0.0000023047885,0.00867233,0.00014860797,0.008109769,0.27480114],"study_design_scores_gemma":[0.025393073,0.0010711278,0.9137473,0.0054024234,0.0014945471,0.012311317,0.0069453255,0.0045602694,0.019361716,0.006373532,0.0028209258,0.0005184561],"about_ca_topic_score_codex":0.00006850262,"about_ca_topic_score_gemma":8.617401e-7,"teacher_disagreement_score":0.27428266,"about_ca_system_score_codex":0.000032278538,"about_ca_system_score_gemma":0.00040791446,"threshold_uncertainty_score":0.4551618},"labels":[],"label_agreement":null},{"id":"W1969648931","doi":"10.1117/1.jmi.1.3.035502","title":"Mechanical stability analysis of carrageenan-based polymer gel for magnetic resonance imaging liver phantom with lesion particles","year":2014,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":24,"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","funders":"","keywords":"Imaging phantom; Elastic modulus; Relaxation (psychology); Carrageenan; Biomedical engineering; Polymer; Medicine; Calibration curve; Composite material; Calibration; Materials science; Modulus; Nuclear magnetic resonance; Nuclear medicine; Chromatography; Physics","score_opus":0.01737294897894677,"score_gpt":0.32084004904677743,"score_spread":0.3034671000678307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969648931","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":[0.38004413,0.006567074,0.5968545,0.016001802,0.0000946109,0.00023993579,0.000008150014,0.00006706629,0.00012272863],"genre_scores_gemma":[0.96852803,0.00008003392,0.030114297,0.0010760673,0.00015432888,0.0000063418174,0.0000030036092,0.00002610471,0.000011783431],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697715,0.0001588887,0.0008285299,0.00027640213,0.0013648388,0.00039416115],"domain_scores_gemma":[0.99771875,0.0006594306,0.0003376312,0.00032738244,0.0004965282,0.00046028552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002526873,0.00019287955,0.00084833545,0.00034361577,0.000065492386,0.00003468771,0.00025071818,0.000067139095,0.00019543277],"category_scores_gemma":[0.0013693138,0.00012904723,0.0003701396,0.00050998054,0.00038005083,0.00014272345,0.000039232305,0.00045881656,5.7754085e-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":[0.0026361046,0.0015943241,0.22270179,0.00047095242,0.0004069491,0.00042667158,0.00035545576,0.000013619863,0.19574232,0.001755083,0.0012102206,0.5726865],"study_design_scores_gemma":[0.0033608326,0.0005966698,0.015962075,0.00095580413,0.002698125,0.00014565514,0.00016935573,0.73443997,0.24049489,0.00029184876,0.00067860074,0.00020614052],"about_ca_topic_score_codex":0.000077918776,"about_ca_topic_score_gemma":0.000011445717,"teacher_disagreement_score":0.7344264,"about_ca_system_score_codex":0.00008577888,"about_ca_system_score_gemma":0.00030985897,"threshold_uncertainty_score":0.52623934},"labels":[],"label_agreement":null},{"id":"W2138181477","doi":"10.1117/1.jmi.2.4.046501","title":"Implementation methods of medical image sharing for collaborative health care based on IHE XDS-I profile","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Digital Radiography and Breast Imaging","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":"McMaster University","funders":"Chinese Academy of Sciences; Radiological Society of North America","keywords":"Medicine; Image sharing; Metadata; DICOM; Health care; Implementation; Data sharing; Medical record; Image (mathematics); World Wide Web; Computer science; Artificial intelligence; Radiology; Pathology","score_opus":0.044357979112183775,"score_gpt":0.476245355256247,"score_spread":0.43188737614406325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138181477","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":[0.030806594,0.005736304,0.7416479,0.21167785,0.0018946489,0.0017159263,0.000118208336,0.000084268315,0.0063182795],"genre_scores_gemma":[0.83750564,0.00006864736,0.15356632,0.0076082805,0.0010676833,0.000028321558,0.00007863964,0.000059423255,0.000017058555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99556446,0.00018400718,0.0009908414,0.00020741975,0.0027072404,0.00034602475],"domain_scores_gemma":[0.9967127,0.00037515975,0.0006508399,0.00016596314,0.001037736,0.0010576058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005978391,0.00016948275,0.0006240576,0.0004407182,0.00006145739,0.00005759677,0.00032758742,0.000073607094,0.00018817841],"category_scores_gemma":[0.002128854,0.00012646038,0.0002478393,0.0004943658,0.00018463844,0.00033242066,0.000059685568,0.0004984701,0.0000011207973],"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":[0.00075259263,0.00037509395,0.009148143,0.0006298105,0.00015186917,0.00029198878,0.002734312,0.000005572169,0.00022290823,0.00044550168,0.025739778,0.95950246],"study_design_scores_gemma":[0.12547305,0.012476387,0.01658895,0.026978914,0.0016869769,0.007950095,0.27434078,0.38003865,0.05359482,0.0035067117,0.09544114,0.0019235198],"about_ca_topic_score_codex":0.000049108367,"about_ca_topic_score_gemma":0.0000053771105,"teacher_disagreement_score":0.9575789,"about_ca_system_score_codex":0.00021302018,"about_ca_system_score_gemma":0.003015233,"threshold_uncertainty_score":0.5348894},"labels":[],"label_agreement":null},{"id":"W2140603372","doi":"10.1117/1.jmi.2.1.013502","title":"Single-coil magnetic induction tomographic three-dimensional imaging","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","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":"Kimberly-Clark (Canada)","funders":"Kimberly-Clark","keywords":"Electromagnetic coil; Eddy current; Electromagnetic induction; Medicine; Nuclear magnetic resonance; Transverse plane; Conductivity; Tomography; Acoustics; Biomedical engineering; Physics; Radiology","score_opus":0.014594185523754933,"score_gpt":0.22667924471527415,"score_spread":0.2120850591915192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140603372","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":[0.92534673,0.0394106,0.018977964,0.011145243,0.0032159372,0.00011008582,0.0000017872525,0.00028625253,0.0015054242],"genre_scores_gemma":[0.99735564,0.00010270961,0.0010592991,0.0004249606,0.0010162367,0.0000013740563,0.000001219187,0.000028554561,0.000009988071],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974278,0.000042824326,0.00055443554,0.00012709753,0.0014736186,0.00037420643],"domain_scores_gemma":[0.99888444,0.000076154014,0.00011488468,0.000111823894,0.00022306156,0.00058962754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097598834,0.00017651485,0.00027051687,0.00044707026,0.000044653596,0.000063159176,0.00030280277,0.000075860495,0.00011031927],"category_scores_gemma":[0.00022983158,0.00013769865,0.00017726139,0.0006861012,0.00013872018,0.00035893114,0.000042028405,0.00079651887,0.000012528064],"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":[0.000040729294,0.00018231318,0.03219173,0.00004497009,0.000066663655,0.00070993614,0.00010745271,0.00047245304,0.010008339,0.00005546071,0.034856964,0.921263],"study_design_scores_gemma":[0.0069468087,0.000501466,0.02018793,0.0015181256,0.0003525383,0.013894493,0.00039886206,0.884117,0.0063471347,0.015081618,0.04918419,0.0014698113],"about_ca_topic_score_codex":0.000016279217,"about_ca_topic_score_gemma":0.000006611291,"teacher_disagreement_score":0.9197932,"about_ca_system_score_codex":0.00007486797,"about_ca_system_score_gemma":0.00009159382,"threshold_uncertainty_score":0.5615188},"labels":[],"label_agreement":null},{"id":"W2142717410","doi":"10.1117/1.jmi.1.3.031005","title":"Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression","year":2014,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":16,"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; University of California, San Diego; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; National Institutes of Health; Genentech; IXICO; Servier; Instituto Tecnológico y de Estudios Superiores de Monterrey; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Consejo Nacional de Ciencia y Tecnología; Synarc; Bayer HealthCare; Meso Scale Diagnostics; Foundation for the National Institutes of Health","keywords":"Medicine; Magnetic resonance imaging; Neuroimaging; Positron emission tomography; Logistic regression; Artificial intelligence; Cognition; Pattern recognition (psychology); Radiology; Internal medicine; Computer science; Psychiatry","score_opus":0.01009305487226867,"score_gpt":0.30864864598632663,"score_spread":0.29855559111405794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142717410","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":[0.52219504,0.11238381,0.25909418,0.097483605,0.0006599591,0.0073706484,0.00014603841,0.00008280668,0.0005839323],"genre_scores_gemma":[0.9970467,0.00043527628,0.0006584156,0.0013187798,0.00039085926,0.00007421103,0.000022071974,0.00002018799,0.00003350373],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973628,0.0001447831,0.00042830102,0.00022844691,0.0015459943,0.00028965567],"domain_scores_gemma":[0.9980272,0.00035868134,0.00022808685,0.00011325917,0.00074082235,0.0005319276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013008596,0.00016178418,0.00026731842,0.00019694662,0.00015991062,0.00005740476,0.00013251134,0.000035314642,0.00018289397],"category_scores_gemma":[0.0003773889,0.00009280507,0.00009677497,0.0001964796,0.00024664585,0.000159909,0.00006826173,0.00028661077,6.64311e-7],"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":[0.005348787,0.00044869477,0.17925328,0.00016599476,0.00009531632,0.00008788129,0.00063778803,0.000006154452,0.00062517275,0.000021110254,0.005637026,0.8076728],"study_design_scores_gemma":[0.008234495,0.0026017253,0.9407198,0.004581537,0.0011796544,0.00040765505,0.001107897,0.037466206,0.0015898796,0.00017148031,0.001803622,0.00013606895],"about_ca_topic_score_codex":0.0000058479955,"about_ca_topic_score_gemma":0.0000010463723,"teacher_disagreement_score":0.8075367,"about_ca_system_score_codex":0.0000380514,"about_ca_system_score_gemma":0.00020037658,"threshold_uncertainty_score":0.3784481},"labels":[],"label_agreement":null},{"id":"W2175570168","doi":"10.1117/1.jmi.2.4.043502","title":"Model predictions for the wide-angle x-ray scatter signals of healthy and malignant breast duct biopsies","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","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":"Laurentian University","funders":"Compute Canada","keywords":"Medicine; Biopsy; Ductal carcinoma; Radiology; Breast tissue; Mammography; X-ray; Nuclear medicine; Pathology; Breast cancer; Optics; Cancer; Internal medicine","score_opus":0.030384944596647867,"score_gpt":0.3616494652150114,"score_spread":0.33126452061836353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175570168","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":[0.1487107,0.009613382,0.7380251,0.102721855,0.00030086684,0.00027731547,0.000055061566,0.00001177251,0.00028393607],"genre_scores_gemma":[0.9923412,0.0012497222,0.0040322184,0.0016974416,0.0005043443,0.00001253689,0.000005694577,0.000013356822,0.0001434544],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856997,0.000037321668,0.00036934862,0.00012467908,0.0006852872,0.00021339637],"domain_scores_gemma":[0.9989474,0.00013086679,0.00014577707,0.00013632014,0.00028997668,0.00034968747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014826318,0.000083609484,0.00017043744,0.000049594648,0.000052510422,0.000017690178,0.00033517007,0.000093311726,0.000020551819],"category_scores_gemma":[0.00097493315,0.00004786597,0.000092046845,0.0000716231,0.0004995395,0.000007928763,0.00014411684,0.00022262351,2.3098332e-7],"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":[0.0009793269,0.0004520222,0.004225912,0.00017675415,0.00025070496,0.000023364084,0.00019518529,0.00011267558,0.58758056,0.00023550514,0.37509787,0.030670092],"study_design_scores_gemma":[0.0075345435,0.00233339,0.0013050041,0.0011325101,0.00035318852,0.0034161513,0.0018101635,0.37146574,0.4784126,0.015968198,0.11557255,0.0006959645],"about_ca_topic_score_codex":0.000008300518,"about_ca_topic_score_gemma":7.4216507e-7,"teacher_disagreement_score":0.84363055,"about_ca_system_score_codex":0.000019079785,"about_ca_system_score_gemma":0.00031771814,"threshold_uncertainty_score":0.19519177},"labels":[],"label_agreement":null},{"id":"W2175730465","doi":"10.1117/1.jmi.2.4.044002","title":"Quantitative performance characterization of image quality and radiation dose for a CS 9300 dental cone beam computed tomography machine","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Dental Radiography and Imaging","field":"Dentistry","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Centre National de la Recherche Scientifique","keywords":"Imaging phantom; Cone beam computed tomography; Image quality; Medicine; Nuclear medicine; Pixel; Scanner; Image resolution; Dot pitch; Ionization chamber; Optics; Medical physics; Computed tomography; Physics; Artificial intelligence; Radiology; Ionization; Image (mathematics); Computer science","score_opus":0.026465653397229946,"score_gpt":0.3378828525717866,"score_spread":0.3114171991745566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175730465","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":[0.9252324,0.0014619565,0.07173455,0.00054971944,0.00077825197,0.00013740444,0.00004675607,0.000013621521,0.000045307836],"genre_scores_gemma":[0.99688244,0.00018097524,0.00244041,0.00023941183,0.00018238854,0.0000022198067,0.000052708565,0.0000139008025,0.0000055673604],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979128,0.00011871272,0.00079324807,0.00013464468,0.00086593017,0.00017462617],"domain_scores_gemma":[0.9983341,0.0001760842,0.0007850428,0.000085741725,0.00034448202,0.0002745321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018960892,0.00012790729,0.00036636047,0.00033681333,0.00006770033,0.000076198245,0.000192857,0.000050359937,0.000021808783],"category_scores_gemma":[0.0004430176,0.0001125343,0.00015759245,0.0003093166,0.00023857095,0.0009391449,0.00004979298,0.00026250942,0.0000014765021],"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":[0.0015299194,0.00061945914,0.7299522,0.00064305164,0.00035196223,0.00014707241,0.0018175424,0.000007706319,0.12087063,0.0003373608,0.0012431903,0.14247988],"study_design_scores_gemma":[0.008726957,0.00030062645,0.84789526,0.0005736843,0.00016050841,0.0012092912,0.0013646731,0.12668447,0.011913631,0.00021833695,0.0006742981,0.00027825046],"about_ca_topic_score_codex":0.000027938757,"about_ca_topic_score_gemma":0.0000024743792,"teacher_disagreement_score":0.14220163,"about_ca_system_score_codex":0.000034107736,"about_ca_system_score_gemma":0.00007257561,"threshold_uncertainty_score":0.45890155},"labels":[],"label_agreement":null},{"id":"W2277068097","doi":"10.1117/1.jmi.3.1.016001","title":"Fabrication and control of CT number through polymeric composites based on coronary plaque CT phantom applications","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Advanced X-ray and CT Imaging","field":"Engineering","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":"University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University Health Network","keywords":"Imaging phantom; Fabrication; Hounsfield scale; Biomedical engineering; Medicine; Thermoplastic polyurethane; Polyvinylidene fluoride; Materials science; Composite material; Polymer; Computed tomography; Nuclear medicine; Radiology; Elastomer","score_opus":0.005083402073368344,"score_gpt":0.2559191669791666,"score_spread":0.2508357649057983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277068097","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":[0.02031952,0.0014749753,0.973899,0.0034553534,0.00012946506,0.00008348862,0.000007640738,0.000036254576,0.00059431116],"genre_scores_gemma":[0.99602246,0.00036992633,0.0029150948,0.0004869788,0.00017281315,0.000006528967,0.0000012439734,0.000018870538,0.000006072545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988864,0.000035291116,0.00039131282,0.00008976476,0.00043902532,0.00015820647],"domain_scores_gemma":[0.9990735,0.0004519895,0.00016657819,0.000111684894,0.00006731466,0.00012895207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022819774,0.00011260032,0.00023533033,0.00006442236,0.000044035372,0.00001202146,0.00016351764,0.000015420439,0.00011205542],"category_scores_gemma":[0.00007644622,0.0000791741,0.000059383394,0.000107755455,0.00012669338,0.00029241826,0.00001286009,0.00020209696,0.00000618304],"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":[0.00008853169,0.00019671698,0.04992527,0.0001268434,0.00011973323,0.00016206574,0.00012749872,0.0017603461,0.08012197,0.0005716048,0.00096873904,0.86583066],"study_design_scores_gemma":[0.017408665,0.00014820701,0.029282182,0.0038461639,0.00040916353,0.0046116277,0.00054651924,0.76299155,0.11943863,0.00411065,0.05615926,0.0010473786],"about_ca_topic_score_codex":0.000004142008,"about_ca_topic_score_gemma":1.9236991e-7,"teacher_disagreement_score":0.97570294,"about_ca_system_score_codex":0.000044153232,"about_ca_system_score_gemma":0.000038382637,"threshold_uncertainty_score":0.3228626},"labels":[],"label_agreement":null},{"id":"W2523271765","doi":"10.1117/1.jmi.3.3.034004","title":"Automatic basal slice detection for cardiac analysis","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Cardiac Valve Diseases and Treatments","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":"York University","funders":"National Medical Research Council; National University Health System","keywords":"Basal (medicine); Medicine; Ventricle; Segmentation; Artificial intelligence; Magnetic resonance imaging; Cardiac magnetic resonance imaging; Biomedical engineering; Cardiology; Computer science; Internal medicine; Radiology","score_opus":0.009005072385496663,"score_gpt":0.3604322117590711,"score_spread":0.35142713937357445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523271765","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":[0.89449435,0.001601849,0.09255667,0.010076714,0.00087783585,0.0001852203,0.000016291175,0.00002675321,0.00016430949],"genre_scores_gemma":[0.99842983,0.00014291673,0.00031824448,0.0003934046,0.0006468846,0.00000597703,0.0000019128715,0.000011674841,0.00004916443],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99852973,0.000043182423,0.0003545279,0.00009901201,0.0008075622,0.00016600278],"domain_scores_gemma":[0.9988372,0.0002522469,0.0001818247,0.00011887499,0.00020634125,0.00040356416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006447904,0.00008531779,0.00042718972,0.0002338107,0.00004017132,0.000015801796,0.00006291563,0.000040455918,0.00025950596],"category_scores_gemma":[0.0008288798,0.000047172525,0.0018831375,0.00021023703,0.000038717095,0.00010547116,0.000016602871,0.000088301684,0.000007726435],"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":[0.00011951471,0.00012754652,0.23411955,0.000046101788,0.0052963966,0.00008129686,0.00003242614,5.7469947e-7,0.000977551,0.000008173016,0.0017011308,0.75748974],"study_design_scores_gemma":[0.009019311,0.00021691137,0.9347346,0.00074096076,0.03025614,0.0001811443,0.00028101896,0.01715254,0.0016412982,0.00029249862,0.005281486,0.00020211613],"about_ca_topic_score_codex":0.0000125937395,"about_ca_topic_score_gemma":6.267944e-7,"teacher_disagreement_score":0.7572876,"about_ca_system_score_codex":0.00011841434,"about_ca_system_score_gemma":0.00013949603,"threshold_uncertainty_score":0.28414083},"labels":[],"label_agreement":null},{"id":"W2553903516","doi":"10.1117/1.jmi.3.4.046003","title":"Preterm neonatal lateral ventricle volume from three-dimensional ultrasound is not strongly correlated to two-dimensional ultrasound measurements","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Neonatal and fetal brain pathology","field":"Medicine","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":"London Health Sciences Centre; Western University","funders":"Canadian Institutes of Health Research; Academic Medical Organization of Southwestern Ontario","keywords":"Medicine; Ventricle; Lateral ventricles; Ultrasound; Nuclear medicine; Cerebral ventricle; Cardiology; Anatomy; Radiology","score_opus":0.018902915533489967,"score_gpt":0.2777786952486552,"score_spread":0.2588757797151653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553903516","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":[0.976824,0.00079724245,0.002820404,0.017445507,0.0016811845,0.00021682068,0.000094517796,0.00003592512,0.00008443959],"genre_scores_gemma":[0.99019057,0.0000090206995,0.0017405025,0.006624813,0.0009454444,0.000003158112,0.000022693925,0.000042727646,0.00042107078],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99493223,0.00011997576,0.000999646,0.0004361543,0.0029117388,0.00060027104],"domain_scores_gemma":[0.9970609,0.00071263034,0.00037281465,0.00027832633,0.00045098015,0.001124329],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010912414,0.0003165765,0.00065027084,0.00023236692,0.00012197762,0.000040307455,0.00039688323,0.000147889,0.0042863176],"category_scores_gemma":[0.001650413,0.00020357591,0.00030093646,0.00020132259,0.00028543617,0.0003582922,0.00016501003,0.0007582023,0.00032232018],"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":[0.0040517976,0.00057682226,0.4264259,0.000035601333,0.0007595084,0.009531377,0.0005341926,0.000045731565,0.40729517,0.000017657847,0.029037442,0.1216888],"study_design_scores_gemma":[0.05395924,0.0017627236,0.78755546,0.006160708,0.0012178073,0.07487491,0.00013294684,0.008463421,0.04518418,0.0021072114,0.01688346,0.001697955],"about_ca_topic_score_codex":0.00008235531,"about_ca_topic_score_gemma":0.000012454687,"teacher_disagreement_score":0.36211097,"about_ca_system_score_codex":0.00022439506,"about_ca_system_score_gemma":0.00037107198,"threshold_uncertainty_score":0.9966239},"labels":[],"label_agreement":null},{"id":"W2562549310","doi":"10.1117/1.jmi.3.4.044005","title":"Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","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":"Western University","funders":"","keywords":"Segmentation; Convexity; Geodesic; Image warping; Artificial intelligence; Image segmentation; Topology (electrical circuits); Computer vision; Computer science; Mathematics; Algorithm; Combinatorics; Geometry","score_opus":0.01509246697781281,"score_gpt":0.30533885768861574,"score_spread":0.29024639071080294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562549310","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":[0.081371926,0.00013262322,0.8987439,0.01921335,0.00029903816,0.00013035996,0.000002170998,0.000030177223,0.0000764236],"genre_scores_gemma":[0.8955202,0.0003553331,0.10129477,0.0026722618,0.0001288285,0.000005970669,8.7322206e-7,0.000012254647,0.000009479682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965193,0.00030222943,0.00094219396,0.00019227668,0.0018150018,0.00022895416],"domain_scores_gemma":[0.9980279,0.000717625,0.00052712526,0.00016097908,0.00025893832,0.00030739006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039246366,0.00012934388,0.00033061177,0.00016298801,0.000048046033,0.000073163435,0.0008061022,0.00007839486,0.0007600478],"category_scores_gemma":[0.0024054516,0.00007412482,0.000055199795,0.00021757885,0.0011956528,0.0008034721,0.00026332066,0.0004555759,0.0000029269836],"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":[0.00002223654,0.000105212916,0.002261308,0.000037740636,0.000023452081,0.0002176038,0.0009354332,5.3736187e-8,0.024020549,0.00044514143,0.0013476324,0.9705836],"study_design_scores_gemma":[0.02930968,0.00094688253,0.03048085,0.010314336,0.00012198403,0.009167102,0.008850281,0.74342895,0.14650781,0.018773861,0.00096824695,0.0011300074],"about_ca_topic_score_codex":0.000044622124,"about_ca_topic_score_gemma":0.0000117446725,"teacher_disagreement_score":0.96945363,"about_ca_system_score_codex":0.00009706347,"about_ca_system_score_gemma":0.00028721403,"threshold_uncertainty_score":0.83219904},"labels":[],"label_agreement":null},{"id":"W2563437364","doi":"10.1117/1.jmi.3.4.044505","title":"Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Retinopathy of Prematurity Studies","field":"Medicine","cited_by":15,"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":"Natural Sciences and Engineering Research Council of Canada; Universidade Federal de Alagoas","keywords":"Medicine; Retinopathy of prematurity; Tortuosity; Retinal; Fundus (uterus); Ophthalmology; Receiver operating characteristic; Quadrant (abdomen); Retinopathy; Radiology; Surgery; Gestational age; Internal medicine","score_opus":0.01869066111123832,"score_gpt":0.2994506669126495,"score_spread":0.28076000580141114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563437364","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":[0.98035604,0.0025001839,0.010716066,0.005788218,0.00034725264,0.00020398344,0.0000069912594,0.000008113659,0.000073148345],"genre_scores_gemma":[0.99419266,0.0019195812,0.0035996693,0.000062685576,0.00020229422,0.0000028865913,0.0000010444594,0.000014751785,0.000004436118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9958116,0.00026828467,0.0017162703,0.00020421605,0.0017652577,0.00023437546],"domain_scores_gemma":[0.9965602,0.000700608,0.0014999149,0.00033342987,0.0007028249,0.00020299455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003466426,0.00016848341,0.00103673,0.00035721957,0.000022972255,0.0000059575814,0.00034475917,0.00011654659,0.00007491996],"category_scores_gemma":[0.0035277093,0.00011312423,0.00028615343,0.00021803586,0.0005285635,0.00020358589,0.00015029602,0.00039556637,0.0000010907728],"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":[0.00037760922,0.00048508134,0.8418309,0.000747063,0.0001384123,0.00020583851,0.0007874301,9.006957e-7,0.0719571,0.000018103947,0.0006246592,0.08282689],"study_design_scores_gemma":[0.0034459173,0.00037404054,0.85132295,0.009684534,0.00022927593,0.00034659088,0.00018451408,0.0006828634,0.13331614,0.00021202049,0.00007317275,0.00012795362],"about_ca_topic_score_codex":0.00007316065,"about_ca_topic_score_gemma":0.0000042647243,"teacher_disagreement_score":0.082698934,"about_ca_system_score_codex":0.000075873795,"about_ca_system_score_gemma":0.00016135578,"threshold_uncertainty_score":0.46130723},"labels":[],"label_agreement":null},{"id":"W2580932265","doi":"10.1117/1.jmi.4.1.014001","title":"Deformable image registration for tissues with large displacements","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","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":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Hospital for Sick Children","keywords":"Medicine; Image registration; Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.016046479981313068,"score_gpt":0.36251565676395037,"score_spread":0.3464691767826373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580932265","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":[0.000635947,0.00009062127,0.98386407,0.014217861,0.0002849442,0.00012574249,0.0000012837472,0.0000330143,0.0007465041],"genre_scores_gemma":[0.22499652,0.00017070265,0.77151394,0.002199469,0.0005647077,0.000017986407,0.0000041093895,0.000020587535,0.0005119542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978284,0.000035946177,0.00042811505,0.0001380788,0.001321394,0.00024806577],"domain_scores_gemma":[0.99823844,0.000078961624,0.0007596025,0.00036778103,0.00028402897,0.0002712098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002278525,0.000095383315,0.00018623461,0.0000793904,0.00030276575,0.000582942,0.0014511618,0.000036992045,0.000063562824],"category_scores_gemma":[0.0011014229,0.00006497674,0.00005875426,0.000043295313,0.00013479567,0.0026221662,0.0001628357,0.00022806444,0.000004137141],"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":[0.0001151167,0.0003805187,0.0047763786,0.0001892267,0.000103967264,0.00083091436,0.0009112983,0.0000015643019,0.0037861995,0.0066823876,0.11167876,0.87054366],"study_design_scores_gemma":[0.018784987,0.0010128774,0.0052823164,0.0034113207,0.00017665738,0.0028357592,0.00091298117,0.71690387,0.15168889,0.013331279,0.084599234,0.001059837],"about_ca_topic_score_codex":0.00001254415,"about_ca_topic_score_gemma":0.0000049603395,"teacher_disagreement_score":0.8694838,"about_ca_system_score_codex":0.000045421028,"about_ca_system_score_gemma":0.00018797576,"threshold_uncertainty_score":0.5621323},"labels":[],"label_agreement":null},{"id":"W2593719472","doi":"10.1117/1.jmi.4.1.015002","title":"Effects of line fiducial parameters and beamforming on ultrasound calibration","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Ultrasound Imaging and Elastography","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":"Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Imaging phantom; Calibration; Fiducial marker; Medicine; Beamforming; Context (archaeology); Biomedical engineering; Artificial intelligence; Computer vision; Nuclear medicine; Radiology; Computer science; Physics","score_opus":0.009472792263935215,"score_gpt":0.2926336720856835,"score_spread":0.2831608798217483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593719472","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":[0.97138464,0.00064225896,0.017114645,0.009873914,0.0007298212,0.00008545805,9.322113e-7,0.00000945095,0.00015884795],"genre_scores_gemma":[0.99493873,0.000499301,0.0031472119,0.00078472274,0.00059961487,7.304112e-7,0.000001161822,0.000013323751,0.000015223994],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99825346,0.000038598737,0.00040525122,0.00011132746,0.0010158385,0.00017550083],"domain_scores_gemma":[0.9984237,0.00057190517,0.00046212546,0.00016042864,0.000087034256,0.00029479814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091768714,0.000112301204,0.0003499708,0.00019138175,0.00015048131,0.00008727341,0.00019354111,0.000067179026,0.000016127877],"category_scores_gemma":[0.0040710694,0.00008139916,0.00013449849,0.000050841278,0.00031922822,0.00028572782,0.00003062257,0.0005168119,6.6830165e-7],"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":[0.001236019,0.0012349298,0.24615356,0.0015573158,0.0007212785,0.00077390997,0.0033356901,0.000033406774,0.17076936,0.0003530616,0.006115673,0.56771576],"study_design_scores_gemma":[0.05590311,0.005541937,0.53291595,0.039763484,0.004388115,0.025807397,0.0037168676,0.043277506,0.25001293,0.0052437065,0.031591166,0.0018378098],"about_ca_topic_score_codex":0.000056752062,"about_ca_topic_score_gemma":0.0000011691765,"teacher_disagreement_score":0.565878,"about_ca_system_score_codex":0.000018629025,"about_ca_system_score_gemma":0.000098445256,"threshold_uncertainty_score":0.48737445},"labels":[],"label_agreement":null},{"id":"W2594785967","doi":"10.1117/1.jmi.4.2.021104","title":"Differentiation of arterioles from venules in mouse histology images using machine learning","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":9,"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","funders":"Canadian Institutes of Health Research; Cancer Care Ontario; London Health Sciences Centre; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Medicine; Generalizability theory; Artificial intelligence; Digital pathology; Feature selection; Pattern recognition (psychology); Receiver operating characteristic; Segmentation; Feature (linguistics); Computer science; Pathology; Biomedical engineering; Machine learning","score_opus":0.016916334453425434,"score_gpt":0.28996824328127047,"score_spread":0.273051908827845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594785967","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":[0.90618443,0.0022411663,0.08885088,0.0021869629,0.00042190924,0.000024054663,0.0000047750127,0.0000122742795,0.000073558134],"genre_scores_gemma":[0.99308306,0.000062953986,0.0066672,0.00007265014,0.000096207805,2.7739895e-7,0.0000013112409,0.000008192597,0.000008169203],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984977,0.000090255155,0.00048018567,0.00013907877,0.00061844004,0.00017430488],"domain_scores_gemma":[0.99855924,0.000117822434,0.00082522916,0.0002641379,0.00009160643,0.00014195015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004349012,0.00009880359,0.00030028418,0.00019634796,0.00007761042,0.00024315265,0.0011802308,0.000032747474,0.000015054769],"category_scores_gemma":[0.0013820581,0.000086421765,0.000095797004,0.00004415858,0.00019351233,0.0010395382,0.0003303001,0.00030444097,0.0000014837235],"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":[0.000031788277,0.00039226894,0.7162657,0.00004886564,0.00006228046,0.0008370984,0.0005625467,0.000112333844,0.03900541,0.0006078735,0.000117813855,0.24195604],"study_design_scores_gemma":[0.0030021511,0.00005363472,0.25318864,0.0012646375,0.000068640315,0.0003574083,0.000111950685,0.71192986,0.021462318,0.0077478862,0.00048407965,0.00032882192],"about_ca_topic_score_codex":0.00031539367,"about_ca_topic_score_gemma":0.000014252148,"teacher_disagreement_score":0.7118175,"about_ca_system_score_codex":0.000045387398,"about_ca_system_score_gemma":0.00011541249,"threshold_uncertainty_score":0.3524177},"labels":[],"label_agreement":null},{"id":"W2613447076","doi":"10.1117/1.jmi.4.4.041305","title":"Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection","year":2017,"lang":"en","type":"preprint","venue":"Journal of Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"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; Sunnybrook Hospital; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Radiomics; Patient privacy; Computer science; Artificial intelligence; Deep learning; Machine learning","score_opus":0.013623607691825938,"score_gpt":0.34138648453084536,"score_spread":0.32776287683901945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613447076","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":[0.10883296,0.032770813,0.8139204,0.031003682,0.012220389,0.0010121211,0.000039203966,0.000070419424,0.00013000761],"genre_scores_gemma":[0.95444447,0.020901537,0.011058907,0.0015850558,0.010740973,0.00016857535,0.00007764291,0.0001971119,0.0008257395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942474,0.00021505343,0.001758411,0.00072038424,0.002221307,0.0008374809],"domain_scores_gemma":[0.99484766,0.0006348512,0.0024117304,0.0006751968,0.00060983,0.00082071556],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0039015694,0.00058234815,0.0015277533,0.00042805483,0.0005826772,0.00072921754,0.0011992016,0.00065831526,0.000031454794],"category_scores_gemma":[0.0069046146,0.00044526008,0.0010857891,0.000097318065,0.0007937524,0.00092056155,0.0006354471,0.0044866866,0.0000013213289],"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":[0.0022543592,0.00049912126,0.02660782,0.0026397738,0.0016050565,0.0040466795,0.00030495322,0.0014007128,0.021975333,0.00016160624,0.010941744,0.92756283],"study_design_scores_gemma":[0.006150073,0.00020277104,0.008329931,0.0072578024,0.0016591807,0.007112288,0.00009434957,0.95180196,0.0006732524,0.00576104,0.010143,0.00081435515],"about_ca_topic_score_codex":0.00013780227,"about_ca_topic_score_gemma":0.000014669908,"teacher_disagreement_score":0.95040125,"about_ca_system_score_codex":0.0015085898,"about_ca_system_score_gemma":0.0027462535,"threshold_uncertainty_score":0.9997999},"labels":[],"label_agreement":null},{"id":"W2756068656","doi":"10.1117/1.jmi.4.3.036001","title":"Ex vivo tissue imaging for radiology–pathology correlation: a pilot study with a small bore 7-T MRI in a rare pigmented ganglioglioma exhibiting complex MR signal characteristics associated with melanin and hemosiderin","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":7,"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 Winnipeg; University of Manitoba; Health Sciences Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Manitoba Medical Service Foundation","keywords":"Hemosiderin; Medicine; Ex vivo; Magnetic resonance imaging; Pathology; Diffusion MRI; In vivo; Melanin; Radiology; Biology","score_opus":0.07248924691438242,"score_gpt":0.35460723132288585,"score_spread":0.28211798440850344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756068656","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":[0.7446953,0.00012430453,0.2376088,0.016521556,0.00004846361,0.00086508255,0.000015862388,0.00005669165,0.00006389376],"genre_scores_gemma":[0.9824569,0.000035744626,0.01646212,0.00075312454,0.00017163609,0.00003984481,0.000017309796,0.000045226596,0.000018090284],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981353,0.0001034641,0.00065722177,0.00031823886,0.00043722047,0.0003485152],"domain_scores_gemma":[0.9978711,0.00033677815,0.0009654569,0.0002604992,0.00032062212,0.00024553778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010094404,0.00022633527,0.0006576057,0.00024675264,0.00027894165,0.00008215413,0.0002560746,0.00004501097,0.000029196926],"category_scores_gemma":[0.00078824704,0.00017182805,0.00003519416,0.00014318574,0.0003554629,0.00018461263,0.00010882153,0.00067353883,2.5585177e-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":[0.0017007063,0.0013712146,0.93849015,0.000102385464,0.00011605885,0.01457855,0.00075772224,0.000011614933,0.017797783,0.0000875643,0.0012341115,0.023752108],"study_design_scores_gemma":[0.03207563,0.0065964954,0.8144746,0.0047883694,0.00072044274,0.040155146,0.0029087698,0.094316624,0.0013173159,0.0005139825,0.0013836714,0.00074898114],"about_ca_topic_score_codex":0.000027030115,"about_ca_topic_score_gemma":0.000040129882,"teacher_disagreement_score":0.23776157,"about_ca_system_score_codex":0.00009138705,"about_ca_system_score_gemma":0.00014838857,"threshold_uncertainty_score":0.7006945},"labels":[],"label_agreement":null},{"id":"W2759305182","doi":"10.1117/1.jmi.4.3.031212","title":"Comparison of the CTDI and AAPM report No. 111 methodology in adult, adolescent, and child head phantoms for MSCT and dental CBCT scanners","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Dental Radiography and Imaging","field":"Dentistry","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":"Vancouver Coastal Health; University of British Columbia","funders":"University of British Columbia; American Association of Physicists in Medicine","keywords":"Imaging phantom; Medicine; Cone beam computed tomography; Nuclear medicine; Dosimetry; Task group; Dose profile; Medical physics; Multislice; Computed tomography; Radiology","score_opus":0.05669146769317305,"score_gpt":0.419512734047842,"score_spread":0.36282126635466894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2759305182","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":[0.98987246,0.0039633974,0.0017626104,0.0026098685,0.0013391157,0.00013972499,0.0000061899723,0.0000042608276,0.00030237978],"genre_scores_gemma":[0.998066,0.00029546767,0.0010943365,0.00026294496,0.00024602047,0.0000013497369,8.6298394e-7,0.000012838998,0.000020152185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99815553,0.00015462452,0.0007224744,0.00019778598,0.0005331029,0.00023645771],"domain_scores_gemma":[0.99841154,0.00022116705,0.00085842825,0.00019580564,0.00010431136,0.00020876396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019507455,0.00013557244,0.00050035265,0.00015650755,0.0002306922,0.00011880465,0.00035089665,0.00006967254,0.000009921369],"category_scores_gemma":[0.0022236987,0.000097009295,0.00013872073,0.00006112267,0.0007445567,0.00035860817,0.00020690486,0.00056713755,2.8769347e-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":[0.0001381462,0.00008582776,0.96329457,0.00013786684,0.000052255753,0.00038898352,0.00020947019,3.0212922e-7,0.0008974505,0.000026941922,0.0011513497,0.03361686],"study_design_scores_gemma":[0.0034410842,0.00004953297,0.9683986,0.0017881531,0.00010920041,0.017340861,0.0012729581,0.004981697,0.0014312518,0.00014812096,0.00090516004,0.00013341526],"about_ca_topic_score_codex":0.0001945352,"about_ca_topic_score_gemma":0.00021663154,"teacher_disagreement_score":0.033483446,"about_ca_system_score_codex":0.000021644142,"about_ca_system_score_gemma":0.000039345774,"threshold_uncertainty_score":0.39559242},"labels":[],"label_agreement":null},{"id":"W2762353096","doi":"10.1117/1.jmi.4.4.041306","title":"Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Optical Coherence Tomography Applications","field":"Engineering","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":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Medicine; Optical coherence tomography; Reduction (mathematics); Dimension (graph theory); Ovarian cancer; Tomography; Radiology; Artificial intelligence; Medical physics; Cancer; Internal medicine","score_opus":0.07148382159995947,"score_gpt":0.3812655442851755,"score_spread":0.30978172268521603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762353096","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":[0.646934,0.00015113202,0.35131952,0.00022985137,0.0004503987,0.0008619881,0.000008023343,0.000033168868,0.000011972856],"genre_scores_gemma":[0.9179881,0.000018307826,0.08167374,0.0000040133723,0.00022474487,0.000059014135,0.0000010073009,0.000030252078,7.9540473e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975161,0.000083613086,0.00095733075,0.00026730308,0.0008981309,0.0002775206],"domain_scores_gemma":[0.99784726,0.00014108412,0.000577526,0.0005459796,0.0006105667,0.00027756233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017660646,0.00020064838,0.00045887413,0.00028357247,0.0002779468,0.00011041586,0.000612737,0.0001605116,0.000040635037],"category_scores_gemma":[0.00070648175,0.00017990728,0.00014347213,0.00026208634,0.00028956714,0.000563421,0.00008460046,0.00050149026,3.1644893e-7],"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":[0.00015820615,0.0005024237,0.009528694,0.0001074122,0.00009169718,0.000012264521,0.00021104098,0.0012165089,0.9364625,0.00012271688,0.000046264424,0.05154024],"study_design_scores_gemma":[0.0033391623,0.00031777727,0.082959674,0.0017233527,0.00057941,0.00024199719,0.0011805787,0.76853716,0.13884342,0.0017248644,0.000036872774,0.0005157051],"about_ca_topic_score_codex":0.00020508879,"about_ca_topic_score_gemma":0.000014508666,"teacher_disagreement_score":0.7976191,"about_ca_system_score_codex":0.00021707462,"about_ca_system_score_gemma":0.0001626901,"threshold_uncertainty_score":0.7336406},"labels":[],"label_agreement":null},{"id":"W2781822205","doi":"10.1117/1.jmi.4.4.045501","title":"Comparing search patterns in digital breast tomosynthesis and full-field digital mammography: an eye tracking study","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering; National Eye Institute","keywords":"Medicine; Digital Breast Tomosynthesis; Visual search; Computer vision; Saccadic masking; Digital mammography; Eye movement; Mammography; Radiology; Artificial intelligence; Medical physics; Computer science; Breast cancer; Ophthalmology","score_opus":0.023568319761162044,"score_gpt":0.33246538219919797,"score_spread":0.30889706243803594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781822205","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":[0.99342483,0.00016516422,0.00079326227,0.0042586536,0.0001642364,0.00014817469,0.0000075811595,0.000022508444,0.0010156073],"genre_scores_gemma":[0.99932885,0.00003041486,0.000041843843,0.00015276304,0.00040785377,0.000001408955,0.0000024190206,0.000027812017,0.0000066259277],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99707866,0.000034458644,0.0006906022,0.00028714645,0.0014544636,0.00045466315],"domain_scores_gemma":[0.9982594,0.00018493074,0.0002855206,0.00035019647,0.00016383869,0.0007560886],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011524925,0.00022751406,0.0006124782,0.0006059548,0.00018676126,0.0017873896,0.00054647,0.00006276411,0.00003464753],"category_scores_gemma":[0.0006147816,0.00018247464,0.00021327773,0.00015575517,0.00023335924,0.0036005366,0.00021922406,0.0008953729,0.0000015658472],"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":[0.0001783359,0.00064101524,0.60310227,0.000034348843,0.00007248955,0.002205347,0.0004179547,6.5365987e-7,0.0000410023,0.000004103238,0.000009778472,0.39329273],"study_design_scores_gemma":[0.0029030754,0.0003038268,0.9777658,0.0015875669,0.0000808029,0.007808979,0.005945456,0.0032616083,0.000034100416,0.0000749159,0.000034511762,0.00019936191],"about_ca_topic_score_codex":0.00010772847,"about_ca_topic_score_gemma":0.000032370455,"teacher_disagreement_score":0.39309335,"about_ca_system_score_codex":0.00003610825,"about_ca_system_score_gemma":0.00011030828,"threshold_uncertainty_score":0.99924886},"labels":[],"label_agreement":null},{"id":"W2789347607","doi":"10.1117/1.jmi.5.2.021215","title":"Design and validation of an open-source library of dynamic reference frames for research and education in optical tracking","year":2018,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":21,"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":"Johns Hopkins University","keywords":"Medicine; Open source; Tracking (education); Medical physics; Software","score_opus":0.055397762399397865,"score_gpt":0.3753046797025517,"score_spread":0.31990691730315385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789347607","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":[0.564124,0.00020389279,0.43511796,0.00037223246,0.000045490844,0.0000804891,3.0430394e-7,0.0000032364226,0.000052369298],"genre_scores_gemma":[0.9622659,0.00017992935,0.03746246,0.000018142551,0.00005305962,9.4775726e-7,0.0000025747986,0.000011821849,0.0000051635175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991601,0.00007190881,0.0003241812,0.000064518346,0.0002855643,0.000093717805],"domain_scores_gemma":[0.99931747,0.00030772775,0.000068253976,0.000054541408,0.00015931865,0.00009271673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013378841,0.000042784734,0.00013480255,0.00018805078,0.000023340992,0.00005785015,0.00014071386,0.000051645424,0.000010084945],"category_scores_gemma":[0.00036873246,0.000037868696,0.0000067368137,0.00011589688,0.00012391871,0.00044992162,0.000031484156,0.00018063649,6.0026714e-8],"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":[0.00018886586,0.00026440574,0.0041155,0.00038596036,0.000022647902,0.000006206143,0.0014470185,0.012795414,0.03137377,0.0018443455,0.00039618267,0.9471597],"study_design_scores_gemma":[0.0004289652,0.00015507618,0.002475471,0.0005297477,0.000007034613,0.000026870606,0.00043080322,0.9789124,0.013974654,0.0029542877,0.000061313425,0.000043384276],"about_ca_topic_score_codex":0.0000063413304,"about_ca_topic_score_gemma":9.3894295e-7,"teacher_disagreement_score":0.96611696,"about_ca_system_score_codex":0.000016515698,"about_ca_system_score_gemma":0.00013977157,"threshold_uncertainty_score":0.15442406},"labels":[],"label_agreement":null},{"id":"W2896371716","doi":"10.1117/1.jmi.5.4.044002","title":"Automated segmentation of cellular images using an effective region force","year":2018,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","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 Waterloo","funders":"","keywords":"Pixel; Segmentation; Artificial intelligence; Contrast (vision); Pattern recognition (psychology); Image segmentation; Random walker algorithm; Boundary (topology); Graph; Computer vision; Set (abstract data type); Computer science; Algorithm; Mathematics; Theoretical computer science","score_opus":0.00834267630488138,"score_gpt":0.32507037509599845,"score_spread":0.31672769879111706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896371716","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":[0.6085002,0.00033045854,0.39086524,0.000100152756,0.000041355823,0.00005355393,2.4713628e-7,0.000012686951,0.00009612213],"genre_scores_gemma":[0.9937517,0.000081880404,0.0055314917,0.00016082183,0.0004372414,7.599724e-7,0.000006992698,0.000013512676,0.000015590342],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988477,0.00014600361,0.00035968935,0.00012497377,0.0004021071,0.00011949319],"domain_scores_gemma":[0.99893725,0.000017064827,0.00039893662,0.00015414883,0.0003859253,0.00010669555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080276036,0.00008654639,0.00017701392,0.000111426794,0.000040079543,0.000019462675,0.0002058321,0.00007262135,0.000020107786],"category_scores_gemma":[0.00029161395,0.00007318606,0.00010603536,0.000104192084,0.0001934341,0.000025145608,0.00006401618,0.00011319085,4.7483366e-7],"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":[0.000035317902,0.00006444384,0.001258007,0.000014045891,0.00005302938,0.00004677596,0.00006009491,0.00000519092,0.9755757,0.0000013268889,0.0018239364,0.021062126],"study_design_scores_gemma":[0.00039604513,0.00017492798,0.00029527713,0.00009555312,0.00008190384,0.00022534301,0.0001259522,0.031828307,0.966467,0.000052170446,0.00018565936,0.00007186037],"about_ca_topic_score_codex":0.000026817837,"about_ca_topic_score_gemma":0.0000020370064,"teacher_disagreement_score":0.38533375,"about_ca_system_score_codex":0.000024633011,"about_ca_system_score_gemma":0.000064222026,"threshold_uncertainty_score":0.29844406},"labels":[],"label_agreement":null},{"id":"W2901694925","doi":"10.1117/1.jmi.5.2.026002","title":"Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease","year":2018,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","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":"St. Paul's Hospital; University of Toronto; Sunnybrook Health Science Centre; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Medicine; Magnetic resonance imaging; Radiology; Biomarker; Ventilation (architecture); Nuclear medicine; Biomedical engineering","score_opus":0.01846309291189233,"score_gpt":0.34152964682617076,"score_spread":0.3230665539142784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901694925","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":[0.021418504,0.0008480102,0.97591025,0.0009157998,0.00019848444,0.000111439025,0.00000935094,0.000003839091,0.0005843471],"genre_scores_gemma":[0.99834245,0.000008527907,0.0006158193,0.000040891347,0.00093184726,0.000003954628,0.000006690052,0.000019198082,0.00003063575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997978,0.000041619987,0.00079343305,0.00013384147,0.0007515997,0.00030153443],"domain_scores_gemma":[0.998544,0.00014807751,0.00046426433,0.00013357072,0.00040963182,0.00030046806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012040747,0.00012239323,0.00033182494,0.00013939048,0.000091298585,0.000015502874,0.00039498913,0.000016942226,0.00032744167],"category_scores_gemma":[0.00007928497,0.00009984986,0.00018673415,0.00014432294,0.00025588632,0.00019572611,0.0001479602,0.00021118244,0.0000022538068],"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":[0.000121553996,0.00019394237,0.009684581,0.000120406294,0.00009595017,0.000009451963,0.00032963886,0.0000021857422,0.0068899663,0.0005837823,0.0013288828,0.98063964],"study_design_scores_gemma":[0.00090719055,7.959124e-7,0.00025695554,0.00056978053,0.000044249493,0.0000055063997,0.00043827464,0.99130106,0.0025451207,0.0016373487,0.0021965336,0.00009720513],"about_ca_topic_score_codex":0.000011036881,"about_ca_topic_score_gemma":2.6514684e-7,"teacher_disagreement_score":0.99129885,"about_ca_system_score_codex":0.00010440597,"about_ca_system_score_gemma":0.001730535,"threshold_uncertainty_score":0.4071759},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W2929768480","doi":"10.1117/1.jmi.6.2.021603","title":"Initial evaluation of three-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Coronary Interventions and Diagnostics","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":"Ottawa Hospital; University of Ottawa","funders":"University of California, Irvine; Canon Medical Systems USA","keywords":"Medicine; Fractional flow reserve; Imaging phantom; Pulsatile flow; Coronary artery disease; Scanner; Coronary arteries; Nuclear medicine; Biomedical engineering; Radiology; Coronary angiography; Artery; Artificial intelligence; Cardiology; Computer science","score_opus":0.044256349212677,"score_gpt":0.35998110200748673,"score_spread":0.3157247527948097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929768480","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":[0.9598096,0.001354144,0.03492966,0.002116454,0.0011808941,0.00048365863,0.00001194103,0.0000104693545,0.00010315627],"genre_scores_gemma":[0.9965666,0.000059924147,0.0027788524,0.00025541525,0.000250814,0.000006895775,0.00004755155,0.000016240192,0.000017686869],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99685967,0.00007575997,0.0008248118,0.00012520254,0.0019689656,0.00014559137],"domain_scores_gemma":[0.99710757,0.0005096915,0.000511917,0.00014370824,0.0015598777,0.0001672284],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016331944,0.00010278546,0.00030735048,0.00018662852,0.0000328315,0.000012844126,0.00011765787,0.000043493343,0.0022254398],"category_scores_gemma":[0.002013364,0.00008190252,0.00024569104,0.00010293579,0.00006321108,0.00018843873,0.000051169787,0.0002485796,0.000012953187],"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":[0.0007006715,0.00079815596,0.08333212,0.00017497249,0.00016591983,0.0001663236,0.00012826495,0.00013466508,0.002294506,0.00017251325,0.005397348,0.90653455],"study_design_scores_gemma":[0.060433593,0.0054855985,0.52326345,0.018491201,0.002923117,0.013014959,0.002077417,0.31713647,0.023596227,0.0075959945,0.02513973,0.0008422067],"about_ca_topic_score_codex":0.000005458123,"about_ca_topic_score_gemma":0.0000019063425,"teacher_disagreement_score":0.90569234,"about_ca_system_score_codex":0.00011909302,"about_ca_system_score_gemma":0.0004613495,"threshold_uncertainty_score":0.9986867},"labels":[],"label_agreement":null},{"id":"W2931985496","doi":"10.1117/1.jmi.6.2.025001","title":"Intraoperative 360-deg three-dimensional transvaginal ultrasound during needle insertions for high-dose-rate transperineal interstitial gynecologic brachytherapy of vaginal tumors","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Endometrial and Cervical Cancer Treatments","field":"Medicine","cited_by":21,"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","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Medicine; Brachytherapy; Imaging phantom; Radiology; Ultrasound; Transvaginal ultrasound; Nuclear medicine; Radiation therapy","score_opus":0.01376684624070131,"score_gpt":0.2937769305689417,"score_spread":0.2800100843282404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931985496","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":[0.98521453,0.00067137333,0.0048829997,0.007898094,0.000683191,0.00044470743,0.000031787335,0.000019347854,0.00015399202],"genre_scores_gemma":[0.99692404,0.00011381048,0.0012477759,0.00090890646,0.0006772201,0.000014446999,0.000017756196,0.000029641538,0.000066424036],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9973391,0.0000966975,0.0009452047,0.0002505039,0.0009848921,0.00038358767],"domain_scores_gemma":[0.99810165,0.0006593788,0.00031603815,0.00013646999,0.00036763342,0.00041882085],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00076003216,0.000252307,0.00083300803,0.00029859805,0.00009352881,0.000026384465,0.00023725923,0.000104983745,0.0022392885],"category_scores_gemma":[0.00032697848,0.00017763182,0.0003484301,0.00031980142,0.00023277516,0.0002847486,0.000018617666,0.0006524994,0.000008213469],"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":[0.057805307,0.0074672704,0.309128,0.002449,0.004647828,0.0036353865,0.0032900292,0.0012956503,0.4797258,0.002765748,0.0009515108,0.12683846],"study_design_scores_gemma":[0.16885024,0.013333015,0.68971664,0.004976763,0.0020021414,0.009063605,0.0023282478,0.00901651,0.09529801,0.0027647456,0.0013811374,0.0012689403],"about_ca_topic_score_codex":0.00009985295,"about_ca_topic_score_gemma":0.00004075591,"teacher_disagreement_score":0.38442782,"about_ca_system_score_codex":0.00019559762,"about_ca_system_score_gemma":0.00049635785,"threshold_uncertainty_score":0.9986728},"labels":[],"label_agreement":null},{"id":"W3011129081","doi":"10.1117/1.jmi.7.4.042803","title":"Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":26,"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 Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Upload; Computer science; Software; Pipeline (software); Graphical user interface; Segmentation; Feature (linguistics); Interface (matter); Image processing; Application programming interface; Image file formats; Source code; Machine learning; Artificial intelligence; Image (mathematics); World Wide Web","score_opus":0.021337683740427126,"score_gpt":0.3554667564339915,"score_spread":0.3341290726935644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011129081","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":[0.088153884,0.00971834,0.6483252,0.2527357,0.00044623498,0.00031250555,0.000005297023,0.000115742085,0.00018710423],"genre_scores_gemma":[0.79102015,0.00035436635,0.18113527,0.024874669,0.0024226494,0.000008567374,0.000011064185,0.0001234285,0.000049815666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964514,0.00008011952,0.0010055624,0.00049064093,0.0014115724,0.00056072883],"domain_scores_gemma":[0.997037,0.0004560091,0.0006841172,0.0001671178,0.00067003537,0.0009857535],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026949467,0.00035297914,0.00087236555,0.00030872566,0.00023710477,0.0002862839,0.00044646187,0.00011415739,0.000083376966],"category_scores_gemma":[0.011892565,0.0002789439,0.00032330572,0.00034953956,0.000387773,0.00048012927,0.00015772326,0.0017666903,0.0000020770194],"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":[0.0018148357,0.00030598306,0.12408111,0.003170288,0.0002049505,0.0032149446,0.0019211664,0.00011979268,0.076852255,0.0006612067,0.09524999,0.6924035],"study_design_scores_gemma":[0.0066569843,0.00011124252,0.0006083429,0.0024189504,0.0003325493,0.0013314411,0.0008290157,0.96249646,0.00058869517,0.00022309477,0.024121108,0.00028210747],"about_ca_topic_score_codex":0.000010494089,"about_ca_topic_score_gemma":4.083893e-7,"teacher_disagreement_score":0.96237665,"about_ca_system_score_codex":0.00007522858,"about_ca_system_score_gemma":0.00067419745,"threshold_uncertainty_score":0.99996626},"labels":[],"label_agreement":null},{"id":"W3015288507","doi":"10.1117/1.jmi.7.2.026002","title":"Detectability of fluorescent gold nanoparticles under micro-CT and optical projection tomography imaging","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Photoacoustic and Ultrasonic Imaging","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autofluorescence; Medicine; Colloidal gold; Preclinical imaging; Tomography; Histology; Biomedical engineering; In vivo; Pathology; Melanoma; Nuclear medicine; Microscopy; Gold standard (test); Fluorescence; Radiology; Nanoparticle; Materials science; Optics; Cancer research; Nanotechnology","score_opus":0.008090612711843883,"score_gpt":0.2295080581712676,"score_spread":0.2214174454594237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015288507","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":[0.9369029,0.0032358458,0.055672392,0.003596201,0.00032600388,0.000089995665,0.0000023249572,0.0000635361,0.00011084754],"genre_scores_gemma":[0.9970248,0.00019833514,0.002210057,0.0003598239,0.00018374858,0.0000011722101,3.263086e-7,0.000021387257,3.9866146e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829185,0.00004440292,0.0006033592,0.00014148281,0.00063465483,0.00028424832],"domain_scores_gemma":[0.9991742,0.00016414242,0.00011113715,0.000086084015,0.000089213005,0.000375232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006964204,0.0001485144,0.00031460536,0.00010894747,0.000033928573,0.000045220986,0.00018849225,0.000022924609,0.000034965706],"category_scores_gemma":[0.00039781418,0.00012584454,0.00011801567,0.00023377748,0.0002236984,0.00025066728,0.00005517104,0.0005263529,9.274527e-7],"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":[0.000053392152,0.00009726629,0.031181525,0.00044786005,0.00011234465,0.00028219726,0.0007458461,0.00055805425,0.8274573,0.000046975783,0.0012660917,0.13775113],"study_design_scores_gemma":[0.0017944179,0.000042802345,0.00925827,0.0005475282,0.00018413713,0.002042683,0.002166443,0.645293,0.33758572,0.00027608438,0.0005016635,0.00030723767],"about_ca_topic_score_codex":0.000011062175,"about_ca_topic_score_gemma":7.685263e-7,"teacher_disagreement_score":0.644735,"about_ca_system_score_codex":0.000050803264,"about_ca_system_score_gemma":0.00007619616,"threshold_uncertainty_score":0.5131792},"labels":[],"label_agreement":null},{"id":"W3034780937","doi":"10.1117/1.jmi.7.3.033502","title":"Photon-counting computed tomography of lanthanide contrast agents with a high-flux 330-μm-pitch cadmium zinc telluride detector in a table-top system","year":2020,"lang":"en","type":"article","venue":"Journal of 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":"Redlen Technologies (Canada); University of Victoria","funders":"","keywords":"Cadmium zinc telluride; Imaging phantom; Gadolinium; Nuclear medicine; Detector; Medicine; Contrast (vision); Tomography; Lutetium; Photon counting; Optics; Physics; Materials science; Radiology","score_opus":0.005869698926480241,"score_gpt":0.21303794167369014,"score_spread":0.2071682427472099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034780937","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":[0.7833562,0.0017735552,0.21293417,0.00075546093,0.0005490098,0.0001932022,0.000011794969,0.00014041987,0.00028617756],"genre_scores_gemma":[0.9961267,0.00003914517,0.0030625972,0.00032372505,0.0003864147,0.0000036546521,0.0000026531095,0.00005328774,0.0000018125907],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717987,0.00006538541,0.0009947877,0.00019702189,0.0010749146,0.00048804304],"domain_scores_gemma":[0.9986884,0.00020973361,0.00036914067,0.00013177359,0.00016405674,0.00043692015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068194995,0.00026352453,0.00072865654,0.00029032744,0.000050724364,0.000053193548,0.00043235332,0.00007666552,0.00007185556],"category_scores_gemma":[0.00020186129,0.00022137989,0.00011779503,0.0007375197,0.00009438511,0.0004002863,0.00006406478,0.0009230284,0.0000032697214],"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":[0.001785819,0.0007699808,0.35946447,0.011867612,0.0024715392,0.03102184,0.010747565,0.13457255,0.35201862,0.0004206581,0.0064676674,0.08839167],"study_design_scores_gemma":[0.005537085,0.0000928751,0.005762959,0.005622009,0.00015422315,0.0012495907,0.0027938115,0.9188493,0.058078982,0.000025907477,0.0013106032,0.0005226899],"about_ca_topic_score_codex":0.000094399,"about_ca_topic_score_gemma":0.000014130751,"teacher_disagreement_score":0.7842767,"about_ca_system_score_codex":0.00010975233,"about_ca_system_score_gemma":0.00012693007,"threshold_uncertainty_score":0.902761},"labels":[],"label_agreement":null},{"id":"W3082904399","doi":"10.1117/1.jmi.7.4.044503","title":"Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets","year":2020,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Diabetic retinopathy; Medicine; Artificial intelligence; Deep learning; Fundus (uterus); Convolutional neural network; Retinal; End-to-end principle; Retinopathy; Pattern recognition (psychology); Computer science; Optometry; Machine learning; Ophthalmology; Diabetes mellitus","score_opus":0.01755500321217589,"score_gpt":0.31950088647375113,"score_spread":0.30194588326157523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082904399","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":[0.52913827,0.0013105557,0.41076356,0.058012046,0.0003996128,0.00023193556,0.000014562576,0.00006456681,0.000064901236],"genre_scores_gemma":[0.992646,0.0000955345,0.0025848704,0.0034207925,0.0011425356,0.0000066576467,0.000030635954,0.00003311803,0.00003985213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733835,0.00011172871,0.00067362963,0.00026085554,0.0011563807,0.0004590784],"domain_scores_gemma":[0.9978676,0.00039899058,0.0003307699,0.00014122507,0.0002654648,0.0009959679],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0019413454,0.00016828517,0.0005342799,0.00013760086,0.00023258314,0.00009691495,0.00025252235,0.000064824584,0.00015979995],"category_scores_gemma":[0.010512367,0.00013556289,0.00026597688,0.00036495607,0.00009948867,0.0002301099,0.00010543101,0.00089519506,0.000020837337],"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":[0.0007728822,0.00016899106,0.04970847,0.00042504794,0.00021968962,0.0016571493,0.0027521008,0.00037055853,0.05268125,0.000002288214,0.004366955,0.8868746],"study_design_scores_gemma":[0.0071303635,0.0007722249,0.005651948,0.0012028703,0.0007794401,0.002003716,0.008023941,0.81344676,0.029004572,0.000042509055,0.13151926,0.00042242187],"about_ca_topic_score_codex":0.000028693856,"about_ca_topic_score_gemma":0.000003582513,"teacher_disagreement_score":0.8864522,"about_ca_system_score_codex":0.000078270146,"about_ca_system_score_gemma":0.000092488146,"threshold_uncertainty_score":0.9978225},"labels":[],"label_agreement":null},{"id":"W3130028400","doi":"10.1117/1.jmi.8.1.015501","title":"Automatic segmentation and tracking of biological prosthetic heart valves","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Cardiac Valve Diseases and Treatments","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 Victoria","funders":"","keywords":"Medicine; Tracking (education); Segmentation; Computer vision; Artificial intelligence; Biomedical engineering; Cardiology","score_opus":0.02209697312046613,"score_gpt":0.3841512512949033,"score_spread":0.36205427817443714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130028400","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":[0.9889821,0.005710002,0.00028962537,0.0047848932,0.00011570748,0.00005729838,0.0000013898414,0.000005680782,0.000053323885],"genre_scores_gemma":[0.99785066,0.00040826827,0.0011892268,0.00044451523,0.00009263417,8.873132e-7,0.0000032263308,0.0000052490163,0.0000053242798],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989325,0.00006051347,0.00032478137,0.000072833565,0.0005182026,0.0000911901],"domain_scores_gemma":[0.999389,0.00009215881,0.00012736503,0.00005512109,0.00013613267,0.00020025249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032686733,0.000059563325,0.00028279013,0.000051089726,0.00002164262,0.000014729979,0.000025052183,0.000029632418,0.00020683839],"category_scores_gemma":[0.0004916643,0.000039748906,0.0003237093,0.00007254261,0.0000746632,0.00006277443,0.000023292978,0.00012166367,0.0000010720909],"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":[0.000036704867,0.00034737526,0.75326246,0.00015361578,0.00029495786,0.0005494422,0.00021663605,5.098172e-7,0.0070040924,0.00002921567,0.0002443583,0.23786065],"study_design_scores_gemma":[0.0031091475,0.00012635683,0.9835421,0.0011763151,0.0006180589,0.003745872,0.0011618093,0.0017157885,0.004311395,0.00030752947,0.00012359064,0.00006207669],"about_ca_topic_score_codex":0.0000024334079,"about_ca_topic_score_gemma":6.747851e-8,"teacher_disagreement_score":0.23779857,"about_ca_system_score_codex":0.000023244116,"about_ca_system_score_gemma":0.00018835739,"threshold_uncertainty_score":0.22647353},"labels":[],"label_agreement":null},{"id":"W3154339073","doi":"10.1117/1.jmi.8.2.024502","title":"Early diagnosis of Alzheimer’s disease on ADNI data using novel longitudinal score based on functional principal component analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Dementia and Cognitive Impairment Research","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":"Simon Fraser University","funders":"National Institute on Aging; National Institutes of Health","keywords":"Medicine; Longitudinal study; Magnetic resonance imaging; Receiver operating characteristic; Disease; Principal component analysis; Lasso (programming language); Population; Internal medicine; Artificial intelligence; Pathology; Radiology","score_opus":0.15167491710602576,"score_gpt":0.4007444366858758,"score_spread":0.24906951957985002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154339073","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":[0.95418763,0.00063002994,0.034994066,0.0096265245,0.00023812444,0.00012184456,0.00005586717,0.0000067394008,0.00013917922],"genre_scores_gemma":[0.9973146,0.000065917615,0.0009583342,0.0011184721,0.00039897265,0.0000029304904,0.000112368994,0.000015037831,0.000013381914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946972,0.00013968111,0.00066584913,0.0003181229,0.003889007,0.00029015384],"domain_scores_gemma":[0.9974601,0.00035693537,0.000314035,0.0004139011,0.0006515821,0.00080339354],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015442594,0.00015758036,0.0005053476,0.0005640498,0.00008292512,0.000046982612,0.0002742146,0.00003664804,0.002492365],"category_scores_gemma":[0.0014352999,0.00012192976,0.00035108472,0.0006212552,0.00017274824,0.00017447243,0.00021280558,0.0006094089,0.000005097369],"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":[0.00089875085,0.0027104968,0.9812082,0.000063933425,0.0015135121,0.003001858,0.000014018829,0.00038453794,0.00046806672,0.000026810187,0.0004925718,0.009217233],"study_design_scores_gemma":[0.003060024,0.00015191743,0.8350813,0.0010729695,0.0036219612,0.00010730061,0.00006295398,0.15579547,0.0007110395,0.000005926761,0.0002400827,0.00008905045],"about_ca_topic_score_codex":0.000033184773,"about_ca_topic_score_gemma":0.0000032686962,"teacher_disagreement_score":0.15541093,"about_ca_system_score_codex":0.00008791406,"about_ca_system_score_gemma":0.001127586,"threshold_uncertainty_score":0.99841946},"labels":[],"label_agreement":null},{"id":"W3159395977","doi":"10.1117/1.jmi.8.s1.014502","title":"Deep CNN models for predicting COVID-19 in CT and x-ray images","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); Convolutional neural network; Artificial intelligence; Receiver operating characteristic; Area under curve; Deep learning; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pattern recognition (psychology); Nuclear medicine; Pneumonia; Computed tomography; Radiology; Pathology; Computer science; Disease; Internal medicine","score_opus":0.0363969574671866,"score_gpt":0.37276085800279246,"score_spread":0.33636390053560583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159395977","genre_codex":"commentary","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":[0.100106955,0.012330474,0.29729274,0.58919483,0.00063300476,0.00028072516,0.0000046547843,0.000046331475,0.00011026001],"genre_scores_gemma":[0.9078108,0.0014986157,0.011529658,0.07813268,0.0009157784,0.00001062969,0.0000047274816,0.000042773616,0.000054359312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975773,0.00011015459,0.0007283594,0.00024312786,0.0010166323,0.00032444415],"domain_scores_gemma":[0.99658495,0.0019148972,0.00025411046,0.00015753788,0.00025713324,0.0008313939],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002616395,0.00014294812,0.0005075547,0.0002770262,0.00008005159,0.0000658288,0.00015014896,0.000050205046,0.00012686246],"category_scores_gemma":[0.01614432,0.00012200285,0.00014412472,0.0002344019,0.0001375693,0.00028464803,0.00011061686,0.00065916934,8.0593094e-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":[0.000796369,0.0019611844,0.36879525,0.0043698666,0.0005491304,0.06320203,0.006545452,0.013027455,0.007140208,0.00045228205,0.12045087,0.41270992],"study_design_scores_gemma":[0.012186609,0.000108788874,0.0060169366,0.0030805138,0.00035415785,0.009404909,0.0022708382,0.9190099,0.0007739341,0.003010361,0.043522935,0.000260109],"about_ca_topic_score_codex":0.00007748386,"about_ca_topic_score_gemma":0.000021495436,"teacher_disagreement_score":0.90598243,"about_ca_system_score_codex":0.00031790222,"about_ca_system_score_gemma":0.0017087661,"threshold_uncertainty_score":0.9921431},"labels":[],"label_agreement":null},{"id":"W3209589623","doi":"10.1117/1.jmi.8.5.052115","title":"Flat-panel conebeam CT in the clinic: history and current state","year":2021,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":39,"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":"Radboud Universitair Medisch Centrum; University of Toronto; University of Texas MD Anderson Cancer Center; University of Twente; Georgia Institute of Technology; Johns Hopkins University; Radboud Universiteit; Emory University","keywords":"Medicine; Flat panel detector; Digital radiography; Flat panel; Medical physics; Image quality; High resolution; Nuclear medicine; Scanner; Radiography; Radiology; Computer science; Detector; Artificial intelligence; Computer graphics (images); Image (mathematics)","score_opus":0.03705924452277223,"score_gpt":0.33347861423877395,"score_spread":0.2964193697160017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209589623","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":[0.8707427,0.1131439,0.0012682475,0.005719703,0.0064058104,0.0000732467,0.0000056405734,0.00001454156,0.0026262247],"genre_scores_gemma":[0.9938517,0.0032108575,0.00009320742,0.002293067,0.00039460257,0.0000010399481,0.0000028073616,0.000012970905,0.00013973155],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99767256,0.00026293675,0.000647268,0.00013977109,0.0010537559,0.0002237331],"domain_scores_gemma":[0.99898744,0.0003601923,0.00025558568,0.00013916413,0.00006675852,0.00019083453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021299534,0.000105415485,0.0002562531,0.00014806262,0.00004447592,0.00007925634,0.00032159354,0.000016816466,0.00059108954],"category_scores_gemma":[0.00055786164,0.00007445826,0.00013334,0.00015507561,0.00021975297,0.0003015999,0.00008261345,0.0010948379,0.000022069487],"study_design_candidate":"not_applicable","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":[0.000036953377,0.00025217037,0.12128408,0.00009368493,0.00006316653,0.024612239,0.0012964037,8.4534076e-7,0.00039191352,0.00006631381,0.1312092,0.72069305],"study_design_scores_gemma":[0.0041925735,0.000028295444,0.21604025,0.0011249488,0.00013432938,0.039807383,0.0034594587,0.002892114,0.0001896378,0.0014657186,0.73034334,0.00032191208],"about_ca_topic_score_codex":0.0000250583,"about_ca_topic_score_gemma":0.000017678442,"teacher_disagreement_score":0.7203711,"about_ca_system_score_codex":0.00007651402,"about_ca_system_score_gemma":0.0002322314,"threshold_uncertainty_score":0.6472016},"labels":[],"label_agreement":null},{"id":"W4308460469","doi":"10.1117/1.jmi.9.6.066001","title":"Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests","year":2022,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","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":"Western University","funders":"National Cancer Institute","keywords":"Medicine; Concordance; Cohort; Stage (stratigraphy); Radiology; Lung cancer; Nomogram; Cancer; Oncology; Internal medicine","score_opus":0.018243162863232416,"score_gpt":0.3565861531020472,"score_spread":0.3383429902388148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308460469","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":[0.98422533,0.0036920349,0.005631044,0.00445408,0.0017268877,0.00020679148,0.00000534288,0.000012963186,0.00004551529],"genre_scores_gemma":[0.997078,0.00057168026,0.0011124549,0.00062565174,0.0005641152,0.0000063925345,0.0000044879253,0.00003177677,0.00000541857],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636495,0.00030804824,0.0008729996,0.00023539578,0.0018226819,0.00039591047],"domain_scores_gemma":[0.9983393,0.00042481802,0.00050239376,0.00010840663,0.00013657736,0.000488517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003776478,0.00017257784,0.00059390784,0.00036947112,0.0002117086,0.000042897846,0.00026308783,0.00005684463,0.00015829311],"category_scores_gemma":[0.0030893348,0.00014846477,0.0001200993,0.0003021174,0.00016733402,0.00019891554,0.0002557329,0.0022918126,1.1740002e-7],"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":[0.00031999053,0.00017121035,0.9186307,0.0000914161,0.000055061562,0.0005005059,0.00063127244,0.0016345314,0.00010644931,0.000006437112,0.000197558,0.07765485],"study_design_scores_gemma":[0.0106091825,0.0000480144,0.19166975,0.00070978946,0.000116054725,0.0007175575,0.0003101046,0.7953534,0.000007933494,0.000031741914,0.00030990652,0.00011654052],"about_ca_topic_score_codex":0.00080793834,"about_ca_topic_score_gemma":0.00001671241,"teacher_disagreement_score":0.7937189,"about_ca_system_score_codex":0.00037375288,"about_ca_system_score_gemma":0.0004944475,"threshold_uncertainty_score":0.99569124},"labels":[],"label_agreement":null},{"id":"W4318695000","doi":"10.1117/1.jmi.10.1.017501","title":"Single patch super-resolution of histopathology whole slide images: a comparative study","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","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 Waterloo","funders":"","keywords":"Magnification; Histopathology; Artificial intelligence; Digital pathology; Image resolution; Image quality; Computer science; Medicine; Pixel; Digital imaging; Computer vision; Digital image; Deep learning; Image processing; Pattern recognition (psychology); Medical physics; Image (mathematics); Pathology","score_opus":0.04251881914739823,"score_gpt":0.3364908861053369,"score_spread":0.29397206695793865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318695000","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":[0.41964945,0.00045344883,0.5670151,0.010581087,0.0018693848,0.00012302579,0.0000012270117,0.00007234822,0.00023494253],"genre_scores_gemma":[0.9966725,0.000021088985,0.0028621906,0.00015284836,0.00023692728,0.0000031918958,3.635372e-7,0.0000071039403,0.00004376203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973678,0.00030627617,0.0006443337,0.0001917563,0.001252198,0.00023763084],"domain_scores_gemma":[0.9986677,0.00025003828,0.0004066322,0.0002335156,0.0002914736,0.00015060304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022584067,0.000103236314,0.00034572842,0.0003306578,0.00007824339,0.000043678167,0.0008006891,0.000045566867,0.000021916747],"category_scores_gemma":[0.00037276413,0.00008817866,0.00009518886,0.00064697466,0.00014884584,0.00054751727,0.000265158,0.00044071893,0.00002110309],"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":[0.00019923157,0.0028127085,0.06303422,0.00013894032,0.0001822641,0.0056661223,0.05692453,0.0014162019,0.13749655,0.00042923258,0.12535931,0.6063407],"study_design_scores_gemma":[0.007251776,0.0024937668,0.08740272,0.0008823142,0.00014894558,0.0037256302,0.019695507,0.83020586,0.019881632,0.004454527,0.02315994,0.00069740904],"about_ca_topic_score_codex":0.0000722398,"about_ca_topic_score_gemma":0.000017441744,"teacher_disagreement_score":0.82878965,"about_ca_system_score_codex":0.00019365603,"about_ca_system_score_gemma":0.00020001944,"threshold_uncertainty_score":0.35958216},"labels":[],"label_agreement":null},{"id":"W4321097043","doi":"10.1117/1.jmi.10.1.016002","title":"In vivo measurements of lung function using respiratory-gated micro-computed tomography in a smoke-exposure model of chronic obstructive pulmonary disease","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","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":"St. Paul's Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; British Columbia Lung Association","keywords":"Medicine; Air trapping; Expiration; Lung; Lung volumes; Functional residual capacity; Respiratory system; Pulmonary function testing; COPD; Parenchyma; Respiratory disease; In vivo; Nuclear medicine; Pathology; Radiology; Internal medicine","score_opus":0.039986318345074344,"score_gpt":0.32749248667756764,"score_spread":0.2875061683324933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321097043","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":[0.9906356,0.006155247,0.001681362,0.0006747542,0.0003392209,0.00041067236,0.000024741186,0.000018031966,0.000060422568],"genre_scores_gemma":[0.9992913,0.000068078,0.00021440588,0.000119724595,0.00024391529,0.0000051195425,0.0000066207326,0.00004234542,0.000008469006],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99470633,0.000305613,0.0013927253,0.00033971973,0.0027248748,0.000530762],"domain_scores_gemma":[0.9977394,0.00012928223,0.0005749237,0.00030309032,0.0006474752,0.0006058399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020283957,0.00024499776,0.0007421908,0.0020630667,0.00004401326,0.000012164302,0.00033763438,0.00013215456,0.00015561895],"category_scores_gemma":[0.00061546813,0.0002268828,0.0003141201,0.0021042374,0.0003672406,0.00042733777,0.00017733678,0.0009319563,8.356347e-7],"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":[0.0020972302,0.00092772435,0.8111969,0.0019126042,0.00025524263,0.002645133,0.0002531519,0.0067997472,0.16044591,0.00001523223,0.00024557768,0.013205538],"study_design_scores_gemma":[0.004141256,0.000055368524,0.42577225,0.0028292087,0.00020942894,0.0000847661,0.00025689072,0.5627138,0.0033848337,0.00038499988,0.000018554514,0.00014862884],"about_ca_topic_score_codex":0.0001053512,"about_ca_topic_score_gemma":0.000009084896,"teacher_disagreement_score":0.55591404,"about_ca_system_score_codex":0.0009355589,"about_ca_system_score_gemma":0.0023282592,"threshold_uncertainty_score":0.9252012},"labels":[],"label_agreement":null},{"id":"W4379388013","doi":"10.1117/1.jmi.10.3.034505","title":"Automated fatty liver disease detection in point-of-care ultrasound B-mode images","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","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":"University of Guelph; Toronto General Hospital","funders":"Mitacs","keywords":"Medicine; Fatty liver; Steatosis; Artificial intelligence; Radiology; Receiver operating characteristic; Ultrasound; Liver disease; Internal medicine; Disease; Computer science","score_opus":0.01071872462260449,"score_gpt":0.3182379100767617,"score_spread":0.30751918545415724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379388013","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":[0.9912295,0.0039283205,0.00026158424,0.00390525,0.00031205532,0.000166551,0.000015667834,0.00008652207,0.00009455361],"genre_scores_gemma":[0.99775445,0.0016507022,0.0001007833,0.00026901375,0.0001813586,0.00000584072,0.000014114705,0.000015785126,0.000007922002],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981888,0.00007418318,0.0004546798,0.00012812352,0.00095356803,0.00020065943],"domain_scores_gemma":[0.99875426,0.00025727673,0.00017924389,0.00012141895,0.00018662898,0.00050118985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003630754,0.00011292072,0.00031111704,0.00032022683,0.000032787564,0.000018691902,0.00010345741,0.000042258995,0.00017367657],"category_scores_gemma":[0.0009282805,0.0000829424,0.00020065624,0.0003124661,0.00008424037,0.00017788015,0.00003670147,0.00025365982,0.000021709791],"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":[0.0005621373,0.0009941817,0.83955497,0.0007006184,0.00025031704,0.014915393,0.0013276852,0.00005277281,0.0028629752,0.000012943933,0.005499487,0.1332665],"study_design_scores_gemma":[0.0031786636,0.00008259495,0.973136,0.0016327462,0.0003500476,0.00023097786,0.0009100716,0.016058914,0.004118347,0.000065189626,0.00014831751,0.00008813758],"about_ca_topic_score_codex":0.00009193098,"about_ca_topic_score_gemma":0.000010150021,"teacher_disagreement_score":0.133581,"about_ca_system_score_codex":0.00014657811,"about_ca_system_score_gemma":0.00024984014,"threshold_uncertainty_score":0.33822933},"labels":[],"label_agreement":null},{"id":"W4380046642","doi":"10.1117/1.jmi.10.3.034003","title":"Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Pediatric Urology and Nephrology Studies","field":"Medicine","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":"Queen's University; University of British Columbia","funders":"Kidney Foundation of Canada","keywords":"Medicine; Ultrasonography; Radiology; Medical physics","score_opus":0.031546805353333526,"score_gpt":0.3431633781709748,"score_spread":0.31161657281764127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380046642","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":[0.90702456,0.0031095978,0.00012587711,0.08892058,0.00050678116,0.00013379645,0.000002002773,0.00003074889,0.00014606336],"genre_scores_gemma":[0.9762422,0.00049413356,0.0012099494,0.02183791,0.00018840187,0.0000069496286,0.0000014139573,0.000012140554,0.000006914774],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99714446,0.000117055926,0.00076857227,0.00016087554,0.0014429351,0.00036609318],"domain_scores_gemma":[0.9983768,0.00029609227,0.00022278586,0.00014005425,0.0002577742,0.00070648873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025151188,0.00014335546,0.0006090562,0.00093193416,0.00007262261,0.0000039403053,0.00017389272,0.000068622365,0.0002957432],"category_scores_gemma":[0.0017800136,0.000108410924,0.0002155788,0.0009958353,0.00024850926,0.00007082442,0.000077160024,0.0005309797,0.000018645827],"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":[0.00028227808,0.000503714,0.36095798,0.000051436942,0.0007469575,0.00082495034,0.0019800188,0.00007266381,0.0068731047,0.000011460959,0.62060386,0.007091587],"study_design_scores_gemma":[0.036818177,0.0015497652,0.8136034,0.0046134414,0.0020054786,0.00387976,0.005999883,0.087918304,0.005710942,0.0012006407,0.035645552,0.001054671],"about_ca_topic_score_codex":0.000042474952,"about_ca_topic_score_gemma":0.0000073289657,"teacher_disagreement_score":0.5849583,"about_ca_system_score_codex":0.00004201152,"about_ca_system_score_gemma":0.00049964566,"threshold_uncertainty_score":0.4420869},"labels":[],"label_agreement":null},{"id":"W4380627353","doi":"10.1117/1.jmi.10.3.036003","title":"Random matrix theory tools for the predictive analysis of functional magnetic resonance imaging examinations","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"Nova Scotia Health Authority; St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Massachusetts General Hospital; University of Toronto; University of Oxford; St. Francis Xavier University","keywords":"Medicine; Magnetic resonance imaging; Radiology; Nuclear magnetic resonance; Medical physics; Nuclear medicine","score_opus":0.03693710430310345,"score_gpt":0.31755322854044743,"score_spread":0.280616124237344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380627353","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":[0.050824467,0.014055365,0.78932625,0.13967209,0.003957838,0.0008242371,0.00022042681,0.00013526814,0.0009840273],"genre_scores_gemma":[0.996706,0.00047389264,0.00011756169,0.0018426181,0.00046527057,0.000039242605,0.0000026172838,0.000016843573,0.00033594013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970185,0.00032463338,0.00057786534,0.0002317007,0.0016014582,0.00024583074],"domain_scores_gemma":[0.9366942,0.06235587,0.00034464788,0.00016436438,0.00035306477,0.000087824425],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0047110613,0.0001222595,0.0003614927,0.0005873796,0.0003359638,0.00007254412,0.00041769093,0.000029675335,0.00023370278],"category_scores_gemma":[0.082690276,0.00008143712,0.0003755966,0.0016326258,0.0004849621,0.00042745774,0.00013491677,0.0003385007,0.0000048222505],"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":[0.0024620118,0.00041344832,0.019443993,0.00009834753,0.0010970664,0.00028059844,0.0025469456,0.015856387,0.024045557,0.022113236,0.09670008,0.81494236],"study_design_scores_gemma":[0.0036064542,0.000079963145,0.17026979,0.00015656832,0.0014537845,0.00017115519,0.0028268273,0.79670525,0.0013233047,0.0064932383,0.016736677,0.00017701504],"about_ca_topic_score_codex":0.000004408068,"about_ca_topic_score_gemma":0.0000030466913,"teacher_disagreement_score":0.94588155,"about_ca_system_score_codex":0.000064222935,"about_ca_system_score_gemma":0.00017981089,"threshold_uncertainty_score":0.9250366},"labels":[],"label_agreement":null},{"id":"W4385235400","doi":"10.1117/1.jmi.10.4.044004","title":"Automatic classification of symmetry of hemithoraces in canine and feline radiographs","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"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; University of Guelph","funders":"","keywords":"Medicine; Radiography; Thorax (insect anatomy); Artificial intelligence; Segmentation; Convolutional neural network; Pattern recognition (psychology); Radiology; Classifier (UML); Nuclear medicine; Anatomy; Computer science","score_opus":0.028467641248825348,"score_gpt":0.37270242707263684,"score_spread":0.3442347858238115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385235400","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":[0.9131095,0.0018587882,0.00027388593,0.08451706,0.00013864809,0.00006916014,0.0000012788948,0.000015393429,0.000016276856],"genre_scores_gemma":[0.99685115,0.0011453414,0.0008579351,0.0010020137,0.00012204495,0.0000012025778,0.0000020545956,0.000012017457,0.000006253716],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99791455,0.000071520444,0.0008323422,0.00009742039,0.0009479456,0.00013621138],"domain_scores_gemma":[0.9983533,0.0007074573,0.0004673279,0.000119351214,0.00017091015,0.00018167325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002356819,0.00007876149,0.00045856036,0.0009206298,0.00001311934,0.000006692441,0.000119247416,0.00005941169,0.000054180397],"category_scores_gemma":[0.0034973905,0.00006218625,0.00008199948,0.00098711,0.0001752366,0.000084438325,0.00004114771,0.00032956738,7.4548785e-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":[0.0000707143,0.0003646637,0.6256307,0.0014590095,0.00010466869,0.00050628954,0.00083000166,0.00001596959,0.03037417,0.000057805537,0.016626997,0.32395905],"study_design_scores_gemma":[0.0033963902,0.00012146538,0.7301097,0.006592083,0.00022410855,0.0005665079,0.0013504924,0.25215158,0.004010738,0.00022036325,0.0011542912,0.00010230494],"about_ca_topic_score_codex":0.000105036444,"about_ca_topic_score_gemma":0.000009415818,"teacher_disagreement_score":0.32385674,"about_ca_system_score_codex":0.00005394925,"about_ca_system_score_gemma":0.00027754804,"threshold_uncertainty_score":0.4186956},"labels":[],"label_agreement":null},{"id":"W4387705767","doi":"10.1117/1.jmi.10.5.054504","title":"Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Respiratory Support and Mechanisms","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":"Université de Montréal; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Institut de Valorisation des Données","keywords":"ARDS; Medicine; Acute respiratory distress; Radiology; Radiography; Lung; Chest radiograph; Internal medicine","score_opus":0.059178157285275666,"score_gpt":0.35342119327685245,"score_spread":0.2942430359915768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387705767","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":[0.9813993,0.0003058308,0.0152685465,0.002382115,0.0003645768,0.00019527337,0.000038833394,0.000019156478,0.000026393429],"genre_scores_gemma":[0.99760765,0.00024777165,0.001256026,0.00057534716,0.00020416104,0.000022247044,0.00004840845,0.000019074887,0.000019337003],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9983918,0.00005074296,0.00061511114,0.00014366131,0.00064875616,0.00014993279],"domain_scores_gemma":[0.99887925,0.00012665614,0.0003823016,0.00013233628,0.00015710008,0.0003223491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011148043,0.00009544946,0.00035246776,0.0002017324,0.000042302272,0.00002440351,0.00010774679,0.00007189504,0.000099011835],"category_scores_gemma":[0.00030596298,0.000075028525,0.00009642893,0.00012939819,0.000078350255,0.00029997414,0.00003481898,0.00020077055,0.000002347879],"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":[0.00040403396,0.0004060487,0.2273155,0.00044246562,0.00050130044,0.0009189266,0.0015959142,0.0000020990756,0.416525,0.00011256051,0.00899761,0.34277853],"study_design_scores_gemma":[0.013352576,0.0027070234,0.75656587,0.0046915887,0.002426603,0.0010515635,0.011175643,0.050568745,0.13900734,0.0019196004,0.015993178,0.00054027175],"about_ca_topic_score_codex":0.000012610099,"about_ca_topic_score_gemma":0.0000018720077,"teacher_disagreement_score":0.5292504,"about_ca_system_score_codex":0.0000445097,"about_ca_system_score_gemma":0.00013938952,"threshold_uncertainty_score":0.30595747},"labels":[],"label_agreement":null},{"id":"W4388660673","doi":"10.1117/1.jmi.10.6.066001","title":"Automated aortic segmentation and quantification of hemodynamic parameters from 4D flow MRI using deep learning techniques","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Cardiac Valve Diseases and Treatments","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":"University of Calgary; University of Guelph","funders":"Siemens Healthineers","keywords":"Medicine; Hemodynamics; Segmentation; Bicuspid aortic valve; Magnetic resonance imaging; Hausdorff distance; Aortic valve; Artificial intelligence; Ground truth; Cardiology; Radiology; Computer science","score_opus":0.016914613684631506,"score_gpt":0.3781518188137209,"score_spread":0.3612372051290894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388660673","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":[0.9566543,0.0005729062,0.041893616,0.00057562796,0.00012330216,0.000094446885,0.0000032550156,0.00007706595,0.000005463918],"genre_scores_gemma":[0.9885281,0.0004578494,0.010840203,0.00005937062,0.000055793265,0.0000015953751,0.00004134328,0.00001388583,0.000001869776],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987093,0.000067601104,0.00040009295,0.00010250259,0.00060586445,0.000114622584],"domain_scores_gemma":[0.99925727,0.000121662866,0.0002812165,0.00007021243,0.000105860294,0.00016380221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041056063,0.0000794673,0.00026367922,0.00019458943,0.000050860766,0.000026765425,0.00004069825,0.00004176409,0.000021837923],"category_scores_gemma":[0.0003109748,0.00006487405,0.00020411734,0.00018635739,0.0000743637,0.00011620083,0.00002395996,0.00016731456,0.0000014099638],"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":[0.00018026255,0.00017846767,0.49395093,0.0001731408,0.0008229478,0.00028410973,0.00057102914,0.0010850157,0.061959732,0.000004836682,0.00009901663,0.44069052],"study_design_scores_gemma":[0.0009184553,0.00003477584,0.13415986,0.00055615016,0.00059484114,0.00007263011,0.0006929165,0.85951626,0.0033363893,0.00006317445,0.0000049341597,0.00004959881],"about_ca_topic_score_codex":0.00009548242,"about_ca_topic_score_gemma":4.7342124e-7,"teacher_disagreement_score":0.8584312,"about_ca_system_score_codex":0.000072928546,"about_ca_system_score_gemma":0.00008351817,"threshold_uncertainty_score":0.26454872},"labels":[],"label_agreement":null},{"id":"W4390607650","doi":"10.1117/1.jmi.11.1.014005","title":"Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"Human Connectome Project; Deconvolution; Diffusion MRI; Magnetic resonance imaging; Artificial intelligence; Regularization (linguistics); Tractography; Orientation (vector space); Deep learning; Computer science; Real-time MRI; Pattern recognition (psychology); Algorithm; Medicine; Mathematics","score_opus":0.03570822091407746,"score_gpt":0.3462099798567345,"score_spread":0.3105017589426571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390607650","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":[0.016658552,0.0044069295,0.9701797,0.0077650277,0.00044215593,0.0003577133,0.000013295883,0.00013663906,0.000040031842],"genre_scores_gemma":[0.7636485,0.00029933924,0.2337512,0.00088062964,0.0010373272,0.000040634524,0.0001930604,0.00006585166,0.00008342785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980551,0.000043570923,0.0006884073,0.00027046527,0.000690623,0.0002518107],"domain_scores_gemma":[0.9988333,0.0002399243,0.00022286523,0.00013909445,0.0003397429,0.00022509885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060003845,0.00016188178,0.00025909857,0.0001353999,0.00016367793,0.000084078274,0.00010049707,0.00007014494,0.00022049814],"category_scores_gemma":[0.0005478573,0.000138673,0.00016379774,0.00039225174,0.00016769754,0.00042531342,0.00003298842,0.0004335677,0.000005020958],"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":[0.00022987966,0.00014057352,0.0013978817,0.000148892,0.000013948284,0.00012209505,0.000057979138,0.00030353453,0.005819253,0.0011909227,0.0034906764,0.9870844],"study_design_scores_gemma":[0.0012574033,0.00007500433,0.0020957817,0.0009221905,0.00025823025,0.00184159,0.000084062245,0.969075,0.00034712715,0.002810496,0.021105364,0.00012770513],"about_ca_topic_score_codex":0.0000074804475,"about_ca_topic_score_gemma":4.184454e-7,"teacher_disagreement_score":0.98695666,"about_ca_system_score_codex":0.00031274534,"about_ca_system_score_gemma":0.00024766274,"threshold_uncertainty_score":0.5654921},"labels":[],"label_agreement":null},{"id":"W4392388989","doi":"10.1117/1.jmi.11.2.026001","title":"Machine learning based prediction of image quality in prostate MRI using rapid localizer images","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Prostate 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":false,"ca_institutions":"University of Ottawa; Vale (Canada); University of Calgary; University of Guelph","funders":"Mitacs; Compute Canada; University of Toronto; Johns Hopkins University","keywords":"Medicine; Artificial intelligence; Image quality; Receiver operating characteristic; Magnetic resonance imaging; Effective diffusion coefficient; Sagittal plane; Prostate; Diffusion MRI; Segmentation; Pattern recognition (psychology); Nuclear medicine; Radiology; Computer science; Image (mathematics)","score_opus":0.02423448289816226,"score_gpt":0.35275924434796835,"score_spread":0.3285247614498061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392388989","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":[0.7235795,0.10038827,0.11028269,0.061731808,0.0020605111,0.00090820406,0.00013061397,0.00013712214,0.0007812706],"genre_scores_gemma":[0.9940051,0.0031614,0.002193418,0.00033696092,0.00023478777,0.000004552727,0.000014484024,0.00002818898,0.000021127216],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734014,0.0001899898,0.0009638015,0.00018515711,0.0010859391,0.00023494192],"domain_scores_gemma":[0.9989318,0.0002458439,0.0002755218,0.00010871085,0.00019135851,0.0002467446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025562472,0.0001489616,0.00049101206,0.0003483487,0.000032150812,0.000043652708,0.00008515722,0.00004944867,0.00036749567],"category_scores_gemma":[0.0005702368,0.00010629013,0.0001942605,0.00030785712,0.00015950551,0.0002754707,0.000037083453,0.00075962115,0.000002068923],"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":[0.001005283,0.0013473323,0.55920106,0.002755489,0.00047906413,0.010907927,0.0012657256,0.0012049834,0.02676665,0.000053902797,0.0031376032,0.39187497],"study_design_scores_gemma":[0.010555708,0.0006028065,0.04834351,0.015239384,0.0005632883,0.001438795,0.0006479943,0.8820911,0.028671302,0.00021271383,0.011385744,0.00024763608],"about_ca_topic_score_codex":0.0001956698,"about_ca_topic_score_gemma":0.0000025123709,"teacher_disagreement_score":0.88088614,"about_ca_system_score_codex":0.00029622825,"about_ca_system_score_gemma":0.0005889055,"threshold_uncertainty_score":0.43343857},"labels":[],"label_agreement":null},{"id":"W4396894282","doi":"10.1117/1.jmi.11.3.036001","title":"Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Anatomy and Medical Technology","field":"Engineering","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":"Queen's University","funders":"NHLBI Division of Intramural Research; National Heart, Lung, and Blood Institute; National Center for Advancing Translational Sciences; Additional Ventures; National Institute on Aging; National Institutes of Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Children's Hospital of Philadelphia","keywords":"Medicine; Open source; Graphical user interface; Interface (matter); Image (mathematics); Computer graphics (images); Computer vision; Software; Programming language","score_opus":0.007015937136375504,"score_gpt":0.31463552522756405,"score_spread":0.30761958809118856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396894282","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":[0.003979621,0.0059138453,0.9477761,0.04137121,0.00054134213,0.00018266388,0.000006718777,0.00006671844,0.00016180049],"genre_scores_gemma":[0.9945965,0.0017427015,0.0029051406,0.00029266006,0.00033078095,0.000028851913,0.0000027424642,0.000033891334,0.00006672137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980453,0.000041068703,0.0007011369,0.00013789596,0.000823686,0.00025087647],"domain_scores_gemma":[0.99728775,0.0020641296,0.00009236999,0.000168767,0.00012167033,0.00026533724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027704437,0.00012358886,0.00043981132,0.00034277,0.000059122784,0.00006826578,0.0010439152,0.00019174037,0.00039747768],"category_scores_gemma":[0.0032330079,0.00007639519,0.00040639186,0.00055731204,0.00040138792,0.00016722508,0.00013318684,0.0007387415,0.0000017817371],"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":[0.00007266741,0.00012686844,0.00021179745,0.00048498207,0.0027609244,0.000110298846,0.0004016333,0.00041326557,0.0004916199,0.012988062,0.04485704,0.93708086],"study_design_scores_gemma":[0.00057166343,0.00003808254,0.00004294099,0.00049426657,0.0008982244,0.00017106079,0.00035924173,0.7331721,0.00089080475,0.0018672473,0.26140815,0.00008620558],"about_ca_topic_score_codex":0.000010737913,"about_ca_topic_score_gemma":0.000010592168,"teacher_disagreement_score":0.9906169,"about_ca_system_score_codex":0.000041486517,"about_ca_system_score_gemma":0.00015401513,"threshold_uncertainty_score":0.4352102},"labels":[],"label_agreement":null},{"id":"W4399230331","doi":"10.1117/1.jmi.11.3.033502","title":"Can processed images be used to determine the modulation transfer function and detective quantum efficiency?","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Photoacoustic and Ultrasonic 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":"Toronto Metropolitan University; Western University","funders":"","keywords":"Medicine; Optical transfer function; Computer vision; Optics","score_opus":0.011067647712694808,"score_gpt":0.2450802035020558,"score_spread":0.234012555789361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399230331","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":[0.40715504,0.0011408211,0.5860679,0.0049179546,0.0005465938,0.00007373487,0.000002188691,0.000055677843,0.00004006822],"genre_scores_gemma":[0.9993053,0.000056441695,0.00006590396,0.00027240842,0.00026777753,0.0000039952592,4.5565886e-7,0.000022192911,0.000005505704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987818,0.000032893764,0.0002882754,0.000107277425,0.0005912353,0.00019854149],"domain_scores_gemma":[0.99939466,0.00030327748,0.000017941953,0.00006133843,0.000062880295,0.00015989612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089726155,0.00011577577,0.00015191018,0.0001441751,0.00008225239,0.00014944735,0.0001332534,0.000034929137,0.00003013284],"category_scores_gemma":[0.00029408096,0.00007548609,0.00005117605,0.00023017137,0.00006336301,0.00026633774,0.000012076733,0.0005150596,0.0000010535639],"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":[0.0000797455,0.00006248959,0.00072311715,0.0006920727,0.00022963433,0.00044871258,0.0107897,0.020757701,0.35410845,0.00026956075,0.0022108722,0.60962796],"study_design_scores_gemma":[0.00033187782,0.000027737035,0.00168533,0.0003666203,0.00008122295,0.0004270538,0.00086020696,0.9917346,0.0034971896,0.0004099427,0.00047166986,0.00010651668],"about_ca_topic_score_codex":0.0000129044365,"about_ca_topic_score_gemma":0.0000060752636,"teacher_disagreement_score":0.97097695,"about_ca_system_score_codex":0.000066337845,"about_ca_system_score_gemma":0.00008852263,"threshold_uncertainty_score":0.30782333},"labels":[],"label_agreement":null},{"id":"W4400452529","doi":"10.1117/1.jmi.11.4.044502","title":"Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Lung Cancer Diagnosis and Treatment","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":"Guelph General Hospital; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Computed tomography; Radiology; Nodule (geology); Tomography; Nuclear medicine; Medical physics","score_opus":0.013137105329178835,"score_gpt":0.3050038500836829,"score_spread":0.2918667447545041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400452529","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":[0.7954891,0.011652589,0.15488014,0.034789223,0.0019465233,0.00043667652,0.0000017843157,0.00012431576,0.0006796858],"genre_scores_gemma":[0.9954975,0.0005497871,0.0007129761,0.0018950073,0.0012759302,0.000011051603,0.0000047471876,0.00004262484,0.0000103802095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946201,0.00018283482,0.00089382374,0.00034312866,0.003537547,0.00042259114],"domain_scores_gemma":[0.9978455,0.0002714477,0.0000644639,0.00012964898,0.00011325988,0.0015756832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002570509,0.00022412273,0.00059355475,0.00081821636,0.00007116062,0.000070665476,0.00024359922,0.00023688187,0.00071175926],"category_scores_gemma":[0.0007600617,0.0001624756,0.0003313225,0.0010152076,0.00013495483,0.00018587313,0.00006919878,0.0019533653,0.000009657091],"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":[0.0003505037,0.0018716338,0.030469133,0.0012431005,0.0004862938,0.046244968,0.0010851198,0.00058885996,0.00055635633,0.000035188892,0.0005692738,0.91649956],"study_design_scores_gemma":[0.0037443303,0.0002170227,0.009328618,0.015244793,0.00028224534,0.022512205,0.00033084876,0.9448775,0.0004483922,0.000033582008,0.002752048,0.0002284008],"about_ca_topic_score_codex":0.00012919772,"about_ca_topic_score_gemma":0.000013898838,"teacher_disagreement_score":0.9442887,"about_ca_system_score_codex":0.00038309593,"about_ca_system_score_gemma":0.0011087937,"threshold_uncertainty_score":0.8486509},"labels":[],"label_agreement":null},{"id":"W4400454076","doi":"10.1117/1.jmi.11.4.044002","title":"Projected pooling loss for red nucleus segmentation with soft topology constraints","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Topological and Geometric Data Analysis","field":"Computer Science","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":"Montreal Neurological Institute and Hospital","funders":"Chinese Government Scholarship; Assistance publique-Hôpitaux de Paris; Agence Nationale de la Recherche; Association France Parkinson; European Commission; China Scholarship Council; EU Joint Programme – Neurodegenerative Disease Research; Biogen; Yale University","keywords":"Medicine; Pooling; Segmentation; Topology (electrical circuits); Nucleus; Artificial intelligence; Combinatorics","score_opus":0.01518282694581315,"score_gpt":0.3083966653326332,"score_spread":0.2932138383868201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400454076","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":[0.0060683666,0.0004833309,0.9611319,0.03164645,0.00045975103,0.000062895,0.000004165536,0.000045996167,0.00009716832],"genre_scores_gemma":[0.93979686,0.00007793158,0.05868536,0.0010091348,0.00036222427,0.0000034080501,0.0000045522324,0.0000066921407,0.00005385632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984868,0.00005552871,0.00038678796,0.00018999103,0.0006586181,0.00022224616],"domain_scores_gemma":[0.9989917,0.00042187193,0.00014651337,0.000105395906,0.0001591235,0.00017538841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010587402,0.00008659382,0.00021581067,0.00034295942,0.00006709046,0.00019974436,0.00058009755,0.00004856293,0.00019753087],"category_scores_gemma":[0.00067641906,0.00005246645,0.00009742232,0.00074207626,0.00019902865,0.00047350224,0.00008963807,0.0002791006,0.00000484591],"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":[0.00002456528,0.00006216165,0.0007957108,0.000040680054,0.00015285147,0.0012939412,0.0002544512,0.000026070295,0.00027260504,0.0071061184,0.003913064,0.98605776],"study_design_scores_gemma":[0.0036229768,0.00057574257,0.0014088519,0.0008510546,0.00038076343,0.010939067,0.0012512119,0.9147089,0.0011048763,0.019097438,0.045524336,0.00053479424],"about_ca_topic_score_codex":0.000010193914,"about_ca_topic_score_gemma":0.0000016781817,"teacher_disagreement_score":0.985523,"about_ca_system_score_codex":0.000042180207,"about_ca_system_score_gemma":0.000228078,"threshold_uncertainty_score":0.21628246},"labels":[],"label_agreement":null},{"id":"W4402687642","doi":"10.1117/1.jmi.11.5.054502","title":"HarmonyTM: multi-center data harmonization applied to distributed learning for Parkinson’s disease classification","year":2024,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; Women and Children’s Health Research Institute; University of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Parkinson Association of Alberta; Alzheimer's Disease Neuroimaging Initiative; Institut de Valorisation des Données; Canada Research Chairs; Parkinson Vereniging; Consortium canadien en neurodégénérescence associée au vieillissement","keywords":"Medicine; Harmonization; Center (category theory); Parkinson's disease; Disease; Artificial intelligence; Pathology","score_opus":0.06497534120814781,"score_gpt":0.3572609854352498,"score_spread":0.292285644227102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402687642","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":[0.021735435,0.0021534334,0.95197797,0.022736227,0.0006953888,0.0004463239,0.00015756792,0.00006768225,0.000030001662],"genre_scores_gemma":[0.9928928,0.00030043244,0.0044327737,0.00085845205,0.0005899042,0.000025369445,0.0007890876,0.00003823149,0.00007294332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980772,0.000039690745,0.00045582643,0.00033053744,0.0008545793,0.00024217482],"domain_scores_gemma":[0.99845076,0.000092612085,0.00013900996,0.00024244055,0.00015919571,0.0009159829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073667406,0.00015138011,0.0002603002,0.00019452792,0.00009274416,0.00012498636,0.00026426298,0.000049484563,0.000108301174],"category_scores_gemma":[0.00089653477,0.00011621137,0.000110675865,0.00021187679,0.000029530862,0.00022600938,0.000101779166,0.00031776234,0.00003579795],"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":[0.0018889124,0.0028256495,0.060809378,0.0009109896,0.00085316715,0.0027383626,0.00043170844,0.0000774508,0.004859715,0.00051197485,0.072823085,0.8512696],"study_design_scores_gemma":[0.004449585,0.000042336465,0.030543173,0.0008934952,0.00057552714,0.000114336115,0.00021771059,0.54439455,0.00013562037,0.00007270565,0.41842774,0.00013321303],"about_ca_topic_score_codex":0.0000021869605,"about_ca_topic_score_gemma":4.9609827e-7,"teacher_disagreement_score":0.9711574,"about_ca_system_score_codex":0.00016315428,"about_ca_system_score_gemma":0.00038359038,"threshold_uncertainty_score":0.4738962},"labels":[],"label_agreement":null},{"id":"W4407992683","doi":"10.1117/1.jmi.12.2.026001","title":"Identifying texture features from structural magnetic resonance imaging scans associated with Tourette’s syndrome using machine learning","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Obsessive-Compulsive Spectrum Disorders","field":"Psychology","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 Calgary","funders":"","keywords":"Medicine; Magnetic resonance imaging; Texture (cosmology); Tourette syndrome; Neuroimaging; Nuclear magnetic resonance; Radiology; Artificial intelligence; Nuclear medicine; Image (mathematics); Psychiatry","score_opus":0.007563108544009804,"score_gpt":0.30452045658985843,"score_spread":0.2969573480458486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407992683","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":[0.88497525,0.088864185,0.009864336,0.012214854,0.0026764148,0.00018951329,0.00001965899,0.00009516823,0.0011006085],"genre_scores_gemma":[0.99680555,0.00005010411,0.0008769646,0.0015192695,0.00026885374,0.000001917159,0.000012868929,0.000058288897,0.00040620466],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961281,0.00053728634,0.0007872055,0.00044544734,0.0014134296,0.00068854436],"domain_scores_gemma":[0.9979671,0.0005274727,0.0007475822,0.00026014756,0.00022963947,0.00026801048],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00082124985,0.00036954158,0.0006985297,0.0005040796,0.00035273583,0.00024272014,0.00082122313,0.0001616522,0.0018715969],"category_scores_gemma":[0.0010915386,0.00029806816,0.00020614265,0.00070554443,0.00034753475,0.00038899545,0.00017025752,0.0027380683,0.000005830734],"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":[0.00023888524,0.00010861492,0.8605005,0.00003187291,0.00040048274,0.009509854,0.002195089,0.00021736945,0.0010056304,0.0003088693,0.0028838862,0.122598946],"study_design_scores_gemma":[0.0058229202,0.00006965362,0.93260664,0.003748298,0.000472267,0.0035763578,0.0038790805,0.046316665,0.00005320311,0.0021053173,0.0008236524,0.00052592915],"about_ca_topic_score_codex":0.0011309363,"about_ca_topic_score_gemma":0.00021848145,"teacher_disagreement_score":0.12207302,"about_ca_system_score_codex":0.0002980752,"about_ca_system_score_gemma":0.000275157,"threshold_uncertainty_score":0.99994713},"labels":[],"label_agreement":null},{"id":"W4408315662","doi":"10.1117/1.jmi.12.s2.s22002","title":"Impact of menopause and age on breast density and background parenchymal enhancement in dynamic contrast-enhanced magnetic resonance imaging","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":1,"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; Health Sciences Centre; Public Health Ontario; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Medicine; Magnetic resonance imaging; Breast MRI; Dynamic contrast; Contrast (vision); Menopause; Radiology; Breast density; Dynamic contrast-enhanced MRI; Parenchyma; Mammography; Nuclear magnetic resonance; Internal medicine; Pathology; Breast cancer","score_opus":0.005971644301848341,"score_gpt":0.29684399067426714,"score_spread":0.2908723463724188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408315662","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":[0.98553306,0.008150093,0.0024399708,0.002693357,0.00012974646,0.00014531317,0.0000050792582,0.000007836307,0.0008955279],"genre_scores_gemma":[0.9984993,0.0007157179,0.00024964905,0.0004397687,0.000045872777,0.0000018518074,0.0000018793825,0.000010824313,0.000035176126],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99799883,0.000066787165,0.0006873996,0.0002332573,0.00067912054,0.00033462245],"domain_scores_gemma":[0.9989877,0.00020772383,0.0002176635,0.00014523955,0.00014100401,0.0003006789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084779283,0.00020308237,0.0006090728,0.00051075226,0.000041232717,0.00006602477,0.00013030211,0.00004320858,0.000042576463],"category_scores_gemma":[0.00020456937,0.00015588931,0.000116754156,0.0003467464,0.0003946174,0.00029024383,0.00007646133,0.0005195184,4.1512516e-7],"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":[0.00081544736,0.0004306801,0.22694072,0.0001372006,0.00006631803,0.0012979074,0.0001924628,0.0000011230564,0.017360508,0.00007558942,0.000151343,0.7525307],"study_design_scores_gemma":[0.0055616666,0.00029995036,0.98005074,0.0046117697,0.00012302911,0.00295556,0.00033357108,0.004667384,0.00073969347,0.00047083403,0.000041696403,0.00014409881],"about_ca_topic_score_codex":0.000084379935,"about_ca_topic_score_gemma":0.000011661171,"teacher_disagreement_score":0.75311005,"about_ca_system_score_codex":0.00013590936,"about_ca_system_score_gemma":0.00023342352,"threshold_uncertainty_score":0.6356982},"labels":[],"label_agreement":null},{"id":"W4409351262","doi":"10.1117/1.jmi.12.5.051803","title":"Correlation of objective image quality metrics with radiologists’ diagnostic confidence depends on the clinical task performed","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Radiology practices and education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Research Nova Scotia; Dalhousie University; Exxon Mobil Corporation","keywords":"Medicine; Correlation; Image quality; Confidence interval; Quality (philosophy); Medical physics; Radiology; Task (project management); Artificial intelligence; Image (mathematics); Internal medicine","score_opus":0.04908256126639332,"score_gpt":0.4387362302467725,"score_spread":0.3896536689803792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409351262","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":[0.86868256,0.0011076048,0.061634637,0.06489186,0.001015391,0.00020675545,0.0000012486885,0.000008162625,0.0024517984],"genre_scores_gemma":[0.9956444,0.0010041124,0.0006190235,0.0023592007,0.00029956235,0.0000028453926,0.0000025857616,0.0000051980533,0.00006307959],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997494,0.0005893303,0.00088214886,0.00013740742,0.000747589,0.00014955265],"domain_scores_gemma":[0.98047596,0.017709237,0.00094350585,0.00020491525,0.00051115395,0.0001552477],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007912074,0.00009609894,0.00044101645,0.00018995407,0.00007838476,0.000016789998,0.00020909375,0.000111511064,0.00011292156],"category_scores_gemma":[0.07602758,0.000050171395,0.0001234418,0.00036831442,0.0005480859,0.0001720208,0.000026054446,0.0012487337,0.000003693275],"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":[0.0012018508,0.00048950955,0.92163575,0.00008070936,0.0003294602,0.0001609852,0.00053185044,0.000026688038,0.00013803139,0.0022059209,0.011345125,0.06185414],"study_design_scores_gemma":[0.0025671863,0.0005619351,0.98007697,0.0009785288,0.00050076883,0.00089318503,0.0036593734,0.008604896,0.0002147153,0.0005844283,0.0012760086,0.000081998034],"about_ca_topic_score_codex":0.00005029314,"about_ca_topic_score_gemma":0.0000031173913,"teacher_disagreement_score":0.12696186,"about_ca_system_score_codex":0.000102014536,"about_ca_system_score_gemma":0.0010191988,"threshold_uncertainty_score":0.9317554},"labels":[],"label_agreement":null},{"id":"W4409654282","doi":"10.1117/1.jmi.12.2.024506","title":"Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","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 Calgary","funders":"Parkinson Association of Alberta; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Medicine; Dimensionality reduction; Reduction (mathematics); Artificial intelligence; Deep learning; Pattern recognition (psychology); Machine learning","score_opus":0.06659836844666897,"score_gpt":0.4031126343954088,"score_spread":0.3365142659487398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409654282","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":[0.3384723,0.00019875563,0.6535156,0.006237654,0.0012916463,0.00021613533,4.098586e-8,0.000015026574,0.000052841577],"genre_scores_gemma":[0.99234664,0.000012385354,0.006947015,0.00026077282,0.00039640602,0.000011872691,0.0000012131978,0.0000049526357,0.000018742403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970245,0.00066334434,0.0006559658,0.00024939657,0.001213733,0.0001931021],"domain_scores_gemma":[0.9986542,0.00018744297,0.00023106964,0.0001860356,0.0006274367,0.00011384395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008463024,0.00008797913,0.0001769121,0.00028496247,0.0001950111,0.00018132312,0.00048272053,0.000038854763,0.000028598422],"category_scores_gemma":[0.002346444,0.00008033105,0.00004761634,0.000439625,0.000042803287,0.0012324076,0.00010332002,0.00042765585,0.0000017245662],"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":[0.00004125936,0.00073602126,0.0077754403,0.000014994284,0.000023735884,0.00013904767,0.002667687,0.025820196,0.008172639,0.0034156465,0.0005243673,0.950669],"study_design_scores_gemma":[0.00048709387,0.000105348765,0.004635387,0.000047066915,0.000019710807,0.0000683312,0.0012666648,0.9892638,0.0012698757,0.0025348654,0.00023375388,0.00006814326],"about_ca_topic_score_codex":0.000055882945,"about_ca_topic_score_gemma":0.00005714231,"teacher_disagreement_score":0.9634436,"about_ca_system_score_codex":0.0001659579,"about_ca_system_score_gemma":0.00038220896,"threshold_uncertainty_score":0.32758048},"labels":[],"label_agreement":null},{"id":"W4410960143","doi":"10.1117/1.jmi.12.3.034504","title":"Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Retinal Imaging and Analysis","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 Calgary","funders":"","keywords":"Medicine; Homomorphic encryption; Diabetic retinopathy; Encryption; Artificial intelligence; Diabetes mellitus; Computer network; Endocrinology","score_opus":0.013731815727531592,"score_gpt":0.30993555343490464,"score_spread":0.29620373770737307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410960143","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":[0.46908662,0.0013415334,0.42934322,0.09896127,0.0005297289,0.00018057274,6.38115e-7,0.00007625561,0.00048015636],"genre_scores_gemma":[0.9965302,0.000060479477,0.0012925272,0.0015692861,0.0002679066,0.000007382945,0.0000090151125,0.000019926703,0.00024330748],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774367,0.00014610396,0.00073442643,0.00018945178,0.00093203457,0.00025430587],"domain_scores_gemma":[0.99803287,0.0004819418,0.0003949932,0.00012010578,0.0007132527,0.00025685073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021723171,0.00013722717,0.00043951752,0.0004734123,0.00020145318,0.000092688984,0.00015403666,0.00007728626,0.000064559055],"category_scores_gemma":[0.0037880798,0.000106138345,0.00028264817,0.00048762074,0.00013952146,0.000048528625,0.00001864977,0.0006959634,0.000007700057],"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":[0.0013202955,0.0018054835,0.24114738,0.0015026578,0.00073222775,0.0009530905,0.00048167075,0.004537794,0.12876691,0.000809522,0.01862065,0.5993223],"study_design_scores_gemma":[0.0029881909,0.000089515066,0.009866917,0.0020255265,0.00055762136,0.000113170536,0.00049457164,0.97616136,0.0027839502,0.00008614047,0.004722506,0.00011052872],"about_ca_topic_score_codex":0.000008136214,"about_ca_topic_score_gemma":2.8679216e-7,"teacher_disagreement_score":0.97162354,"about_ca_system_score_codex":0.00013928743,"about_ca_system_score_gemma":0.0006042208,"threshold_uncertainty_score":0.4534959},"labels":[],"label_agreement":null},{"id":"W4416043472","doi":"10.1117/1.jmi.12.6.064501","title":"Interpretable convolutional neural network for autism diagnosis support in children using structural magnetic resonance imaging datasets","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","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":"Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Autism; Convolutional neural network; Magnetic resonance imaging; Clinical diagnosis; Autism spectrum disorder; Neurodevelopmental disorder; Artificial neural network; Deep learning","score_opus":0.016659508345155373,"score_gpt":0.34039119123856043,"score_spread":0.32373168289340504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416043472","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":[0.87918764,0.024035217,0.020023532,0.072186634,0.002797125,0.0011252251,0.0003366054,0.0000650732,0.00024292205],"genre_scores_gemma":[0.9959791,0.00021652614,0.001058714,0.0024679895,0.00019933333,0.00001419056,0.000013068965,0.000022159704,0.000028948512],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99645466,0.00024353714,0.00085638184,0.00039436796,0.0012257132,0.0008253186],"domain_scores_gemma":[0.9985246,0.00073350983,0.00024984326,0.0002253865,0.000028129049,0.00023854151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013790798,0.00020288903,0.00039232595,0.00037901022,0.0002516431,0.00016411152,0.001054234,0.000051621595,0.000545075],"category_scores_gemma":[0.0019707992,0.00018524185,0.00013574162,0.0006234025,0.00041897217,0.0006956166,0.00043780825,0.00096450734,0.0000023122054],"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":[0.00016897176,0.00010628235,0.86691743,0.00003817098,0.000011571938,0.0005599765,0.00011509304,0.0007576223,0.0006724304,0.0048195445,0.015406589,0.110426314],"study_design_scores_gemma":[0.0029883694,0.000044292115,0.1874842,0.00065187085,0.000040847928,0.0020453052,0.000036605117,0.7884689,0.0004222356,0.014457832,0.0031017812,0.00025773374],"about_ca_topic_score_codex":0.00013923476,"about_ca_topic_score_gemma":0.000015894446,"teacher_disagreement_score":0.7877113,"about_ca_system_score_codex":0.00022308099,"about_ca_system_score_gemma":0.00061685074,"threshold_uncertainty_score":0.75539434},"labels":[],"label_agreement":null},{"id":"W4417348244","doi":"10.1117/1.jmi.12.6.061401","title":"Introduction to the JMI Special Section on Computational Pathology","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Special section; Section (typography); Medical imaging; Computed tomography","score_opus":0.006878304536962735,"score_gpt":0.2903145322760559,"score_spread":0.28343622773909316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417348244","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":[0.0025850392,0.00003769788,0.6989479,0.28316653,0.014712277,0.00003812511,1.11496206e-7,0.000014453355,0.00049782987],"genre_scores_gemma":[0.7802842,0.00007622106,0.023823623,0.03512745,0.16021273,0.000011797719,0.0000011231596,0.000017403638,0.00044541206],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847513,0.00013868307,0.000307951,0.000151493,0.0008058148,0.00012094735],"domain_scores_gemma":[0.9992959,0.00015924871,0.00014446206,0.00014495623,0.00017998175,0.000075448406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014871088,0.00006221,0.000106321975,0.00024232155,0.0001329109,0.000087382316,0.0005409243,0.00004000988,0.00007228563],"category_scores_gemma":[0.00049812277,0.00004334866,0.000055928325,0.00044290238,0.00004682447,0.00022807246,0.00010657405,0.00050471193,0.000021519521],"study_design_candidate":"not_applicable","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":[0.000029199398,0.000033781685,0.00010391721,0.0000035835048,0.000012979279,0.0000431754,0.00026567874,0.007189257,0.000087205335,0.007289044,0.38186458,0.6030776],"study_design_scores_gemma":[0.00085432484,0.00016084746,0.008596378,0.00013959267,0.000017926208,0.0017969969,0.00015765449,0.23699212,0.00061422563,0.01431724,0.7362286,0.00012411785],"about_ca_topic_score_codex":0.0000034766738,"about_ca_topic_score_gemma":0.000004182162,"teacher_disagreement_score":0.77769923,"about_ca_system_score_codex":0.00019973393,"about_ca_system_score_gemma":0.00020564113,"threshold_uncertainty_score":0.21927503},"labels":[],"label_agreement":null},{"id":"W7116952242","doi":"10.1117/1.jmi.13.1.017001","title":"Ultrasound imaging using single-element biaxial beamforming","year":2025,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Ultrasound Imaging and Elastography","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":"Hospital for Sick Children; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrasound imaging; Beamforming; Ultrasound; Ultrasonic imaging; Phase imaging; Imaging technique","score_opus":0.013721595511825994,"score_gpt":0.3097325917478724,"score_spread":0.2960109962360464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116952242","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":[0.50148374,0.006391414,0.45111266,0.033844914,0.003647739,0.00021190489,0.0000031880422,0.00009664663,0.0032078105],"genre_scores_gemma":[0.9743151,0.00030605783,0.01847906,0.005392965,0.001372598,0.000001457788,0.000004554473,0.00004066937,0.00008754084],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958662,0.00008270102,0.0011064706,0.00025678513,0.0020986868,0.0005891653],"domain_scores_gemma":[0.99809086,0.00039419133,0.00041265332,0.00024952478,0.00035024786,0.0005024908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022811648,0.00026467175,0.00059589115,0.000938234,0.00022496635,0.00019073114,0.00041150345,0.00008444894,0.00026519017],"category_scores_gemma":[0.0016144803,0.00021707689,0.00042042468,0.00069888827,0.00031102455,0.00041589077,0.00010523048,0.0011108029,0.0000046121927],"study_design_candidate":"not_applicable","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":[0.0003197803,0.0011900115,0.46826,0.0004536257,0.0007641878,0.00091518596,0.0011983569,0.00008062196,0.21596955,0.00033628772,0.019801216,0.2907112],"study_design_scores_gemma":[0.028247468,0.0004065857,0.023639617,0.028506797,0.004779847,0.08100456,0.021138305,0.09699166,0.02667907,0.003321866,0.68331313,0.001971118],"about_ca_topic_score_codex":0.00009603907,"about_ca_topic_score_gemma":0.0000018693943,"teacher_disagreement_score":0.6635119,"about_ca_system_score_codex":0.0002774877,"about_ca_system_score_gemma":0.0006398602,"threshold_uncertainty_score":0.88521385},"labels":[],"label_agreement":null},{"id":"W784778127","doi":"10.1117/1.jmi.2.2.025002","title":"Three-dimensional nonrigid landmark-based magnetic resonance to transrectal ultrasound registration for image-guided prostate biopsy","year":2015,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Prostate Cancer Diagnosis and Treatment","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":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Magnetic resonance imaging; Landmark; Ultrasound; Radiology; Image registration; Prostate; Biopsy; Prostate biopsy; Artificial intelligence; Image (mathematics); Internal medicine","score_opus":0.02830324013679844,"score_gpt":0.3241517898381175,"score_spread":0.295848549701319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W784778127","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":[0.7605585,0.014488372,0.063997686,0.15679376,0.001540497,0.001940775,0.00010846931,0.00006632641,0.00050565525],"genre_scores_gemma":[0.9370611,0.0001828295,0.055522986,0.0056029293,0.0012500661,0.00011034183,0.000067869994,0.00006739491,0.00013448691],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975167,0.000035523088,0.00063905655,0.00022181479,0.0012756914,0.00031124987],"domain_scores_gemma":[0.9979327,0.00025839178,0.00020553285,0.00015517602,0.0006345917,0.000813633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013383896,0.00017213245,0.0003889143,0.00013370521,0.000055096763,0.000054877055,0.00012916994,0.00006030294,0.00009535733],"category_scores_gemma":[0.0011016164,0.0001264544,0.00016661281,0.00016940128,0.00011123001,0.00014920035,0.00001200908,0.00024918403,0.0000078993335],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008893517,0.0024992689,0.12354587,0.0005165172,0.00023705738,0.0073696123,0.0010736501,0.00016247561,0.01934663,0.0001587811,0.44254497,0.39365166],"study_design_scores_gemma":[0.17400743,0.017317388,0.17728126,0.016941335,0.0028482135,0.04521956,0.0009480979,0.12594235,0.0657674,0.0063759075,0.36478367,0.002567382],"about_ca_topic_score_codex":0.00006286361,"about_ca_topic_score_gemma":0.000026836035,"teacher_disagreement_score":0.39108428,"about_ca_system_score_codex":0.00023644406,"about_ca_system_score_gemma":0.0011488171,"threshold_uncertainty_score":0.51566607},"labels":[],"label_agreement":null}]}