{"meta":{"query_hash":"f526f704ab49","filters":{"venue":"Radiology Artificial Intelligence"},"cohort_total":46,"direct_labels_cover":0,"predictions_cover":46,"exported":46,"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/f526f704ab49","api":"https://metacan.xera.ac/api/v1/cohort?venue=Radiology+Artificial+Intelligence"},"results":[{"id":"W2921682280","doi":"10.1148/ryai.2019180014","title":"Deep Learning for Automated Segmentation of Liver Lesions at CT in Patients with Colorectal Cancer Liver Metastases","year":2019,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs; Fonds de Recherche du Québec - Santé; Polytechnique Montréal","keywords":"Confidence interval; Segmentation; Medicine; Colorectal cancer; Nuclear medicine; Radiology; Lesion; Artificial intelligence; Cancer; Computer science; Internal medicine; Pathology","score_opus":0.05485263043435777,"score_gpt":0.2914274700459204,"score_spread":0.23657483961156262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921682280","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":[0.9969595,0.00053929054,0.0010842619,0.000028000504,0.000083338375,0.0012050094,0.000015951862,0.00004905542,0.000035575333],"genre_scores_gemma":[0.99820673,0.00009819364,0.0012497882,0.000026121059,0.000019277613,0.00012971548,0.00018524376,0.000015645615,0.00006928015],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990104,0.00011765944,0.0002883516,0.00025635547,0.00010732182,0.00021991902],"domain_scores_gemma":[0.99936515,0.00024868446,0.00012636102,0.00008851781,0.00011920686,0.000052102896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014295914,0.00013334177,0.0003215102,0.0001306215,0.00006238779,0.0000039221186,0.000050341634,0.00006356997,0.0006117195],"category_scores_gemma":[0.000059160167,0.00010525461,0.000071548595,0.0001870929,0.00010208222,0.000062467385,0.000020752248,0.00009747446,0.00006785901],"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":[0.0015240399,0.00024060033,0.9679409,0.00006273314,0.000120031975,0.00002499735,0.0005577036,0.0012837314,0.005035235,0.00011947316,0.000019615916,0.023070915],"study_design_scores_gemma":[0.0006894447,0.0028513956,0.5603066,0.00010270649,0.00028865156,0.000016942999,0.00020519628,0.23427913,0.20096642,0.000024775423,0.00006958152,0.00019915214],"about_ca_topic_score_codex":0.00023809193,"about_ca_topic_score_gemma":0.00039026546,"teacher_disagreement_score":0.40763432,"about_ca_system_score_codex":0.00014112468,"about_ca_system_score_gemma":0.00005043734,"threshold_uncertainty_score":0.6697899},"labels":[],"label_agreement":null},{"id":"W3003650457","doi":"10.1148/ryai.2020190007","title":"fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":401,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Artificial intelligence; Art history; Art; Humanities; Computer science","score_opus":0.12957357372301692,"score_gpt":0.35946513002217734,"score_spread":0.22989155629916042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003650457","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":[0.195082,0.0003815448,0.79138994,0.0113917375,0.00009068091,0.00092542666,0.00038651482,0.00018991898,0.00016223884],"genre_scores_gemma":[0.73711103,0.00032545874,0.26055962,0.0007999019,0.0002556732,0.000068558744,0.0007727551,0.000034163095,0.00007284572],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987672,0.00007036019,0.00045747924,0.00039067902,0.00008240376,0.00023187924],"domain_scores_gemma":[0.99910027,0.00018869649,0.00018855723,0.00020315575,0.00014510844,0.0001742356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003568331,0.00013810364,0.000356177,0.00008026133,0.00014763737,0.00002883456,0.00010909051,0.000110586334,0.0004929794],"category_scores_gemma":[0.0008815393,0.00012569095,0.000047041394,0.00026414875,0.00050262356,0.00014440679,0.00007079042,0.00032014915,0.00002029504],"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":[0.0002965389,0.000081186634,0.00355523,0.0001506117,0.000058526537,0.000008231238,0.00015130725,0.000044801316,0.9559379,0.0035562203,0.010906221,0.025253195],"study_design_scores_gemma":[0.00015645879,0.00047414267,0.000041423526,0.00006221389,0.00011644942,0.00027870166,0.00022459705,0.5353735,0.44960856,0.0014883649,0.012000863,0.0001747161],"about_ca_topic_score_codex":0.00015895722,"about_ca_topic_score_gemma":0.0000041412895,"teacher_disagreement_score":0.542029,"about_ca_system_score_codex":0.000020717724,"about_ca_system_score_gemma":0.0000727988,"threshold_uncertainty_score":0.53977793},"labels":[],"label_agreement":null},{"id":"W3032662719","doi":"10.1148/ryai.2020180063","title":"Convolutional Neural Networks for Automatic Risser Stage Assessment","year":2020,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":15,"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":"Convolutional neural network; Stage (stratigraphy); Artificial intelligence; Deep learning; Expert opinion; Computer science; Artificial neural network; Key (lock); Machine learning; Medicine; Intensive care medicine","score_opus":0.10906344221624292,"score_gpt":0.37995304447814326,"score_spread":0.27088960226190034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032662719","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":[0.57104987,0.0011773785,0.40182638,0.022235978,0.00089866,0.0019960552,0.00004689939,0.0002496334,0.0005191551],"genre_scores_gemma":[0.99136126,0.000047167418,0.0042302483,0.0034386502,0.0005275292,0.0002791523,0.000053870997,0.00001855291,0.000043542444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986398,0.000072862895,0.0004385713,0.00037852055,0.00012520791,0.00034501622],"domain_scores_gemma":[0.99899364,0.00040803367,0.000103398925,0.00017318141,0.00010048677,0.00022123828],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018294874,0.000176113,0.00038834783,0.0000451887,0.0001364708,0.000020038475,0.00010771431,0.0001310154,0.00091339933],"category_scores_gemma":[0.00019154984,0.00014887427,0.00016961571,0.00016448642,0.00015210018,0.000050627194,0.000029195688,0.00018880067,0.0000759034],"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":[0.0019007274,0.0031528873,0.23770349,0.00044676766,0.0016785567,0.0004983036,0.0014809903,0.09838398,0.0041186335,0.3913891,0.022248141,0.23699844],"study_design_scores_gemma":[0.00019066998,0.0012842674,0.029381905,0.000022973712,0.00014288005,0.000038689213,0.00019417699,0.9643172,0.0022568884,0.0007966318,0.0012144579,0.00015923554],"about_ca_topic_score_codex":0.0000108893755,"about_ca_topic_score_gemma":0.000008244748,"teacher_disagreement_score":0.86593324,"about_ca_system_score_codex":0.0000963379,"about_ca_system_score_gemma":0.000092523806,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W3035151116","doi":"10.1148/ryai.2020200048","title":"Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT","year":2020,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":158,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Vancouver General Hospital","funders":"AstraZeneca; Universidad de Navarra; National Heart, Lung, and Blood Institute; Sunovion; Houston Methodist Research Institute; GlaxoSmithKline; COPD Foundation","keywords":"Ground-glass opacity; Coronavirus disease 2019 (COVID-19); Ground truth; Medicine; Lung; Opacity; Correlation; Radiology; Pearson product-moment correlation coefficient; Nuclear medicine; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Lobe; Artificial intelligence; Pathology; Internal medicine; Mathematics; Computer science; Statistics; Physics","score_opus":0.12520187806088265,"score_gpt":0.3694180696864446,"score_spread":0.24421619162556193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035151116","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":[0.88268954,0.00013197087,0.06686831,0.04865749,0.00020574444,0.00054211257,0.00016316217,0.0007182546,0.00002341408],"genre_scores_gemma":[0.98342764,0.00004963193,0.0004323849,0.015556551,0.00014277095,0.000032379077,0.00031214146,0.000035194353,0.000011322292],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99774265,0.00028612075,0.00071733986,0.0006544324,0.00026420606,0.00033525424],"domain_scores_gemma":[0.99698216,0.001642231,0.00040596488,0.00043533725,0.000174129,0.00036016337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003932781,0.00024599876,0.0006405274,0.00012679635,0.00011120551,0.0000183906,0.00026411124,0.000106352585,0.0006470608],"category_scores_gemma":[0.0052975877,0.00022221138,0.00010845773,0.0006082815,0.00040923705,0.00007073356,0.00004563316,0.00032450355,0.00018179248],"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":[0.0039684162,0.0032982468,0.59868765,0.0009416688,0.0022384897,0.003078683,0.012492403,0.04112121,0.269974,0.0067396243,0.027884902,0.02957471],"study_design_scores_gemma":[0.0008465612,0.002337593,0.1544007,0.00056783267,0.00091789954,0.00019250688,0.001480437,0.30539834,0.52311313,0.0013987055,0.0083440095,0.001002297],"about_ca_topic_score_codex":0.0013797896,"about_ca_topic_score_gemma":0.00035758934,"teacher_disagreement_score":0.44428694,"about_ca_system_score_codex":0.00024785148,"about_ca_system_score_gemma":0.0005162909,"threshold_uncertainty_score":0.9061517},"labels":[],"label_agreement":null},{"id":"W3084001647","doi":"10.1148/ryai.2020190116","title":"Three-dimensional MRI Bone Models of the Glenohumeral Joint Using Deep Learning: Evaluation of Normal Anatomy and Glenoid Bone Loss","year":2020,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institutes of Health","keywords":"Joint (building); Anatomy; Medicine; Gross anatomy; Orthodontics; Engineering","score_opus":0.12482610048204971,"score_gpt":0.347868425173681,"score_spread":0.22304232469163127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084001647","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":[0.97180855,0.0022670785,0.023643175,0.0015786863,0.00013486232,0.00045954055,0.0000061775445,0.000016963273,0.00008496066],"genre_scores_gemma":[0.99797666,0.000038201128,0.0016842947,0.00016715152,0.00009974305,0.000009271854,0.000008111609,0.00001254359,0.00000403742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834085,0.00019909738,0.000572257,0.00030449338,0.00037394417,0.00020938314],"domain_scores_gemma":[0.9990889,0.00009352694,0.0002479102,0.00019660202,0.00027564733,0.0000974298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005655576,0.00015780762,0.00043084845,0.000065763066,0.00012757599,0.0000040559607,0.000071741124,0.00013491577,0.00018125006],"category_scores_gemma":[0.00020714571,0.000117390744,0.00011685277,0.00022181832,0.0005341677,0.000078680314,0.00008224032,0.00027350656,0.0000056823324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003919087,0.0009994332,0.03692693,0.0002705156,0.0008341099,0.00012708869,0.006143375,0.5848599,0.24130504,0.02690661,0.00006722972,0.097640656],"study_design_scores_gemma":[0.00025978932,0.00062901224,0.0040037897,0.000045523822,0.00056607544,0.00028128992,0.00021012791,0.77233946,0.21141899,0.010121172,0.000012163617,0.000112631664],"about_ca_topic_score_codex":0.00009878361,"about_ca_topic_score_gemma":0.000035413534,"teacher_disagreement_score":0.18747953,"about_ca_system_score_codex":0.00005423513,"about_ca_system_score_gemma":0.00015313012,"threshold_uncertainty_score":0.47870556},"labels":[],"label_agreement":null},{"id":"W3123661268","doi":"10.1148/ryai.2021200254","title":"The RSNA Pulmonary Embolism CT Dataset","year":2021,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":103,"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":"Medicine; Pulmonary embolism; Nuclear medicine; Radiology; Internal medicine","score_opus":0.045093343024980186,"score_gpt":0.324612038351644,"score_spread":0.2795186953266638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123661268","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":[0.67058104,0.038359974,0.029936025,0.16461565,0.0144427465,0.003764748,0.0009571007,0.0007661106,0.07657662],"genre_scores_gemma":[0.9882118,0.0047635105,0.00040607934,0.004134955,0.0007187214,0.00007732432,0.0005510949,0.000026820142,0.0011096753],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983102,0.00015626794,0.0004426298,0.00044353912,0.00018017544,0.00046720047],"domain_scores_gemma":[0.9985915,0.00033526128,0.000080909515,0.0007601143,0.000096216056,0.00013599182],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046984744,0.00017299119,0.00039053516,0.000049566082,0.0004201774,0.00004780901,0.00025296662,0.0000674462,0.0009082038],"category_scores_gemma":[0.0003654628,0.00012649494,0.00011144938,0.00025586854,0.00035764938,0.00006058587,0.00016962127,0.00027816705,0.0010825302],"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":[0.00014576572,0.0010577266,0.000820415,0.000063577754,0.0004290496,0.0042302874,0.00028357378,0.00019469103,0.0072371345,0.3948969,0.13572948,0.4549114],"study_design_scores_gemma":[0.000068980386,0.00022920169,0.012428486,0.00006163744,0.0003089735,0.00243673,0.0015524148,0.0023806868,0.03971126,0.029434685,0.91100925,0.00037770055],"about_ca_topic_score_codex":0.000058092508,"about_ca_topic_score_gemma":0.00008993676,"teacher_disagreement_score":0.77527976,"about_ca_system_score_codex":0.000048604343,"about_ca_system_score_gemma":0.00012967316,"threshold_uncertainty_score":0.99969524},"labels":[],"label_agreement":null},{"id":"W3134917835","doi":"10.1148/ryai.2021210005","title":"Toward a More Quantitative and Specific Representation of Normality","year":2021,"lang":"en","type":"letter","venue":"Radiology Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","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":"Centre Intégré de Santé et Services Sociaux de Chaudière-Appalache","funders":"","keywords":"Generalizability theory; Artificial intelligence; Representation (politics); Medical imaging; Normality; Set (abstract data type); Medical diagnosis; Benchmarking; Computer science; Psychology; Medical education; Medicine; Radiology; Social psychology; Management; Political science; Politics","score_opus":0.20498234250902614,"score_gpt":0.4081260180698439,"score_spread":0.20314367556081775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134917835","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022032745,0.0029121668,0.015197296,0.957939,0.0009415455,0.00063264667,0.00007601879,0.00006388525,0.0002046387],"genre_scores_gemma":[0.08226556,0.0036168518,0.009401196,0.89958566,0.003567414,0.000084955274,0.00087483524,0.00010801046,0.00049553515],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99735063,0.00038136696,0.00082502904,0.00080839253,0.00029443004,0.00034013257],"domain_scores_gemma":[0.9967596,0.0018086537,0.00037063312,0.0005989591,0.00039147257,0.000070704074],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035051745,0.00029526287,0.0009956183,0.0002719258,0.000060283764,0.000022318565,0.00016492346,0.00078264414,0.00044862388],"category_scores_gemma":[0.0012317683,0.00029549265,0.0002033466,0.00043855238,0.0010043621,0.00006540054,0.000100105346,0.0012497107,0.000058281275],"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":[0.00051073154,0.00024085466,0.0017172534,0.001325109,0.00048505326,0.003525687,0.005649513,0.0001342152,0.0079202475,0.0043924386,0.95394725,0.020151652],"study_design_scores_gemma":[0.00037043242,0.00199138,0.011673464,0.0021581103,0.0012444269,0.002283533,0.0044143945,0.0033620258,0.22461486,0.016142031,0.7300492,0.0016961318],"about_ca_topic_score_codex":0.00027205804,"about_ca_topic_score_gemma":0.000017247956,"teacher_disagreement_score":0.22389802,"about_ca_system_score_codex":0.00012833133,"about_ca_system_score_gemma":0.00025122787,"threshold_uncertainty_score":0.9999497},"labels":[],"label_agreement":null},{"id":"W3200676672","doi":"10.1148/ryai.2021210031","title":"A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation","year":2021,"lang":"en","type":"review","venue":"Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"Defence Research and Development Canada","keywords":"Interpretability; Computer science; Artificial intelligence; Segmentation; Machine learning; Deep learning; Data science; Bayesian network; Bayesian probability","score_opus":0.22817201684520388,"score_gpt":0.4874752302087046,"score_spread":0.2593032133635007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200676672","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012259981,0.8978214,0.094627105,0.00070459506,0.001635779,0.004873349,0.00013135756,0.000050478633,0.000033294535],"genre_scores_gemma":[0.0007206765,0.9914795,0.0038583588,0.0008279667,0.0006189015,0.001264138,0.0011634011,0.000050239236,0.00001682184],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9929794,0.0014138025,0.003654149,0.001123591,0.00022753298,0.00060151593],"domain_scores_gemma":[0.9941357,0.002874514,0.0010714463,0.0008140609,0.0009132026,0.00019110534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002278927,0.00056882977,0.0040330687,0.0004623802,0.00010081564,0.000014490086,0.00035768535,0.0009288868,0.0003897753],"category_scores_gemma":[0.0026961737,0.00050392014,0.00095535343,0.0010804055,0.0005912158,0.000101020596,0.000078860336,0.0009791754,0.00003641847],"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":[0.00018269809,0.00041528736,0.00009014221,0.068243764,0.00021398418,0.000015356132,0.00052728667,0.000062055,0.000005251876,0.0005831542,0.00057421747,0.9290868],"study_design_scores_gemma":[0.00021560039,0.0091263745,0.0001270553,0.7213202,0.007736326,0.0025139025,0.0019953898,0.02558368,0.0021630381,0.010875885,0.21555598,0.002786532],"about_ca_topic_score_codex":0.00020086853,"about_ca_topic_score_gemma":0.00013780985,"teacher_disagreement_score":0.9263003,"about_ca_system_score_codex":0.0008853238,"about_ca_system_score_gemma":0.0021789246,"threshold_uncertainty_score":0.99974126},"labels":[],"label_agreement":null},{"id":"W3209826882","doi":"10.1148/ryai.2021210027","title":"Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists","year":2021,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"","keywords":"Medicine; Receiver operating characteristic; Radiology; Lung cancer; Lung cancer screening; Test set; Cancer; Nuclear medicine; Retrospective cohort study; Computed tomography; Artificial intelligence; Surgery; Internal medicine; Computer science","score_opus":0.05653896743861862,"score_gpt":0.3564196165415092,"score_spread":0.2998806491028906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209826882","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":[0.9224019,0.00076489465,0.07525795,0.0005522037,0.00015413569,0.0007675122,0.000021559099,0.00004344842,0.000036376783],"genre_scores_gemma":[0.9978812,0.0002610776,0.0011757051,0.00010611755,0.00017759987,0.00027103935,0.00007834092,0.000018692246,0.000030280768],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984108,0.0002451351,0.00037707784,0.00054601295,0.00012952388,0.00029143665],"domain_scores_gemma":[0.99892426,0.00030355438,0.00016709635,0.00023656804,0.0002623034,0.00010625144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045044665,0.0001682946,0.00040673764,0.00009346127,0.0002188801,0.000021277252,0.000056848345,0.000073452415,0.00003899767],"category_scores_gemma":[0.00022509565,0.0001356154,0.000054397555,0.00024873242,0.00014180694,0.00009636302,0.000021717018,0.00018011482,7.501731e-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":[0.0065787146,0.0029557685,0.8070715,0.00023198471,0.0008475962,0.00025713837,0.0035419993,0.028681066,0.002642228,0.0028444808,0.000036502002,0.144311],"study_design_scores_gemma":[0.0055391984,0.0263352,0.42198497,0.0010122014,0.001469642,0.00064265303,0.01851169,0.3623149,0.15999411,0.00034583893,0.0010021786,0.00084740366],"about_ca_topic_score_codex":0.00029926878,"about_ca_topic_score_gemma":0.01444496,"teacher_disagreement_score":0.38508657,"about_ca_system_score_codex":0.00015010199,"about_ca_system_score_gemma":0.00008994908,"threshold_uncertainty_score":0.8060627},"labels":[],"label_agreement":null},{"id":"W4200475061","doi":"10.1148/ryai.210105","title":"Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes","year":2021,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; University of Toronto; McGill University; Lakeshore General Hospital; McGill University Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Medicine; Pulmonary disease; Radiology; Cardiology; Internal medicine","score_opus":0.037624279706547734,"score_gpt":0.3139190566609588,"score_spread":0.27629477695441107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200475061","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":[0.98245007,0.012153861,0.0035997,0.00041903395,0.00048743153,0.000605373,0.00009363349,0.000028951592,0.00016194461],"genre_scores_gemma":[0.9988336,0.0003009385,0.0002202475,0.00003787415,0.00038276464,0.00002714825,0.000038849805,0.000032798333,0.00012576151],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99768704,0.0003292958,0.00062584603,0.00058818207,0.00031144053,0.00045821405],"domain_scores_gemma":[0.9978661,0.00038581758,0.0002810331,0.0007495805,0.00050060154,0.0002168578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029010794,0.0002518262,0.00051263,0.00013155561,0.0002149616,0.00000773175,0.00024962027,0.0001317271,0.00047154515],"category_scores_gemma":[0.00061634026,0.00020387392,0.00029128502,0.00066330674,0.001441505,0.000116478746,0.00022019334,0.00050099706,0.000013588662],"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":[0.0012590698,0.00035497453,0.0073310705,0.0005558933,0.00021533409,0.00039981454,0.00026273233,0.0007234149,0.9323589,0.0016434437,0.000004078715,0.05489128],"study_design_scores_gemma":[0.000100146404,0.00014607239,0.070634246,0.00011407128,0.00030283627,0.0007148088,0.00070464914,0.07401208,0.84936655,0.003514503,0.00018548538,0.00020455917],"about_ca_topic_score_codex":0.00018863566,"about_ca_topic_score_gemma":0.00017028254,"teacher_disagreement_score":0.082992345,"about_ca_system_score_codex":0.00068097905,"about_ca_system_score_gemma":0.0008494438,"threshold_uncertainty_score":0.8313737},"labels":[],"label_agreement":null},{"id":"W4205996145","doi":"10.1148/ryai.210099","title":"Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine Learning","year":2022,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":9,"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":"Medicine; Convolutional neural network; Artificial intelligence; Radiography; Pipeline (software); Computer science; Machine learning; Radiology","score_opus":0.02155225206151181,"score_gpt":0.24908376306094188,"score_spread":0.22753151099943006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205996145","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":[0.6179687,0.0006726756,0.3808752,0.000047842477,0.00019970637,0.00007078202,0.000004522754,0.00012540906,0.0000351698],"genre_scores_gemma":[0.9993139,0.00010346346,0.0004330289,0.000046870842,0.00005139241,0.000009004304,0.000016299085,0.000017891693,0.0000081512435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989006,0.00020077454,0.0003613674,0.00019838676,0.0001536348,0.00018524632],"domain_scores_gemma":[0.9996382,0.000098652694,0.000052949337,0.00011646027,0.000025247316,0.00006851674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033478707,0.00012506735,0.00026704432,0.00026534192,0.00023655714,0.0000122137535,0.00009922986,0.00005989049,0.000423778],"category_scores_gemma":[0.00011820115,0.00012735509,0.000068841364,0.0005171855,0.00013191215,0.000043741595,0.000031151692,0.00034048455,0.0000042424986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002308246,0.000046390407,0.0027055293,0.00010315095,0.00009055327,0.000011910226,0.00053971354,0.77764297,0.061898492,0.0003323681,0.000009851509,0.15659602],"study_design_scores_gemma":[0.00003762689,0.00012208598,0.00023372332,0.00001961189,0.00004923044,0.000039562427,0.00019380929,0.97890294,0.019713672,0.0004324518,0.00013969404,0.00011561762],"about_ca_topic_score_codex":0.000066366476,"about_ca_topic_score_gemma":0.000011052875,"teacher_disagreement_score":0.3813452,"about_ca_system_score_codex":0.000043982924,"about_ca_system_score_gemma":0.0000093064555,"threshold_uncertainty_score":0.51933897},"labels":[],"label_agreement":null},{"id":"W4224289711","doi":"10.1148/ryai.210115","title":"Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans","year":2022,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Siemens Healthineers","keywords":"Medicine; Radiology; Head (geology); Confidence interval; Nuclear medicine; Internal medicine","score_opus":0.0624108107971913,"score_gpt":0.3564786789809139,"score_spread":0.2940678681837226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224289711","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":[0.43293428,0.00007370393,0.5636877,0.0009663904,0.00040497622,0.0013443264,0.00037079927,0.00007473669,0.00014309362],"genre_scores_gemma":[0.98894644,0.00003495222,0.009419778,0.00034746915,0.0003197593,0.0004483332,0.00008563417,0.000051169878,0.00034649268],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962503,0.0003030626,0.0010441124,0.00085430674,0.0006521465,0.00089611067],"domain_scores_gemma":[0.99728215,0.0013446725,0.00025654974,0.0005260987,0.00031923552,0.0002712732],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010736136,0.00037996445,0.00084699783,0.00037052663,0.0006545778,0.000034970486,0.00043089714,0.00012719838,0.001740525],"category_scores_gemma":[0.00054969575,0.0002954175,0.00017730125,0.00072862726,0.0010692473,0.000109797526,0.000103393555,0.0009957945,0.00007286709],"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":[0.046718158,0.001303823,0.00046203317,0.00035145905,0.00040135902,0.0027235479,0.00074017805,0.0035513854,0.13938205,0.10214844,0.0001262663,0.7020913],"study_design_scores_gemma":[0.00015281179,0.013398144,0.00017348226,0.000060684786,0.00030061204,0.0033960314,0.001422637,0.31061074,0.65959483,0.010395304,0.00006278244,0.00043193216],"about_ca_topic_score_codex":0.00023151416,"about_ca_topic_score_gemma":0.00044590165,"teacher_disagreement_score":0.7016594,"about_ca_system_score_codex":0.00028032556,"about_ca_system_score_gemma":0.0005755811,"threshold_uncertainty_score":0.9999498},"labels":[],"label_agreement":null},{"id":"W4294321765","doi":"10.1148/ryai.220126","title":"Moving from ImageNet to RadImageNet for Improved Transfer Learning and Generalizability","year":2022,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré de Santé et Services Sociaux de Chaudière-Appalache","funders":"","keywords":"Generalizability theory; Artificial intelligence; Deep learning; Transfer of learning; Medicine; Machine learning; Boulevard; Medical imaging; Library science; Computer science; Psychology","score_opus":0.048407051816699576,"score_gpt":0.3411368639542485,"score_spread":0.29272981213754895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294321765","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":[0.7681497,0.0007078655,0.21141061,0.017894557,0.00048873055,0.0010902575,0.000116440155,0.00012560154,0.00001624216],"genre_scores_gemma":[0.9717316,0.000073600866,0.0128329815,0.01418399,0.00039170161,0.00048200926,0.000098906414,0.000050117276,0.00015509487],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99785674,0.00029805637,0.000482317,0.00079685997,0.00012293493,0.00044312063],"domain_scores_gemma":[0.99846286,0.00088526774,0.00004728934,0.00030749146,0.00008800364,0.00020909728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079933903,0.00021574288,0.00045279556,0.00015409676,0.0004863906,0.000029697994,0.00017323441,0.00009772666,0.000912406],"category_scores_gemma":[0.00076465856,0.00023579586,0.00012370758,0.00025718435,0.00018529099,0.00004637453,0.00013597732,0.00038174924,0.000017233328],"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":[0.0015502915,0.0002812885,0.0062752487,0.000059470945,0.00015238851,0.00003206712,0.0026755342,0.017614627,0.8221355,0.00093604065,0.0035096416,0.14477788],"study_design_scores_gemma":[0.00073377375,0.004223387,0.007421191,0.000040834366,0.0004947082,0.00018085973,0.0014942657,0.26256254,0.43164983,0.010030071,0.27999642,0.0011721035],"about_ca_topic_score_codex":0.00045127023,"about_ca_topic_score_gemma":0.00008116476,"teacher_disagreement_score":0.39048567,"about_ca_system_score_codex":0.00022245683,"about_ca_system_score_gemma":0.0001554821,"threshold_uncertainty_score":0.99902064},"labels":[],"label_agreement":null},{"id":"W4307923846","doi":"10.1148/ryai.210294","title":"A Deep Learning Segmentation Pipeline for Cardiac T1 Mapping Using MRI Relaxation–based Synthetic Contrast Augmentation","year":2022,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","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":"St. Michael's Hospital; Health Sciences Centre; Canada Research Chairs; University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Medicine; Segmentation; Artificial intelligence; Nuclear medicine; Contouring; Convolutional neural network; Magnetic resonance imaging; Deep learning; Fluid-attenuated inversion recovery; Contrast (vision); Pattern recognition (psychology); Radiology; Computer science","score_opus":0.04337849740061517,"score_gpt":0.3206126683002549,"score_spread":0.27723417089963975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307923846","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":[0.10526066,0.00054883986,0.89129215,0.0007788294,0.0010703976,0.00082089734,0.00002564465,0.00009892405,0.000103644685],"genre_scores_gemma":[0.9856501,0.000019594529,0.012825573,0.00046162843,0.00034662036,0.00024470635,0.00034390338,0.00003064519,0.000077177996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983227,0.00030949168,0.0004827488,0.00036937091,0.00020544487,0.00031023304],"domain_scores_gemma":[0.99844,0.0009258789,0.00022126213,0.00017025475,0.00016305246,0.00007951896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008378276,0.00015541847,0.00033918672,0.00022155182,0.00059852377,0.00002516503,0.0000671605,0.00006717961,0.00015994861],"category_scores_gemma":[0.0010381764,0.0001786017,0.00018531716,0.00029299004,0.000121373414,0.00005968485,0.000029441273,0.0003053391,0.000027128312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008717383,0.0002004552,0.018712007,0.00010163474,0.0001479171,0.000033000782,0.0028083173,0.82110417,0.07737685,0.0020646015,0.00044789247,0.07613139],"study_design_scores_gemma":[0.00030246523,0.000289093,0.0010097563,0.000043951273,0.0003231629,0.0000914184,0.0060884804,0.962217,0.027481621,0.0007422459,0.00117061,0.00024020576],"about_ca_topic_score_codex":0.000037562742,"about_ca_topic_score_gemma":0.0000026211085,"teacher_disagreement_score":0.8803895,"about_ca_system_score_codex":0.0003529524,"about_ca_system_score_gemma":0.00013339323,"threshold_uncertainty_score":0.7283166},"labels":[],"label_agreement":null},{"id":"W4309321702","doi":"10.1148/ryai.220028","title":"Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls","year":2022,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":131,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; Montreal General Hospital; McGill University Health Centre; University of Calgary","funders":"National Institutes of Health; Fondation de l'Association des radiologistes du Québec","keywords":"Generalizability theory; Computer science; Machine learning; Artificial intelligence; Wilcoxon signed-rank test; Random forest; Overfitting; Feature selection; Artificial neural network; Data mining; Statistics; Mathematics","score_opus":0.38306536606431396,"score_gpt":0.44256822241720156,"score_spread":0.0595028563528876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309321702","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":[0.6120318,0.001431336,0.38465223,0.0007371339,0.0002205247,0.0003307601,0.000009071054,0.0000274512,0.000559691],"genre_scores_gemma":[0.9668601,0.00007201919,0.032767396,0.00013947467,0.00004650734,0.000045951074,0.000034464316,0.000014368122,0.000019681596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953931,0.0025879464,0.0007916331,0.00038267137,0.00060374226,0.00024089818],"domain_scores_gemma":[0.9977637,0.0011609957,0.0003704182,0.0002753542,0.00034842297,0.00008113873],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01018057,0.00014372834,0.000624999,0.00017346352,0.00016975994,0.0000031972818,0.00021754969,0.00008780934,0.0013213361],"category_scores_gemma":[0.0049775452,0.00012624923,0.0001713369,0.00036015958,0.00057532813,0.000048433463,0.00014157461,0.0008010471,0.000003901308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073879596,0.0003888506,0.013406813,0.000059559756,0.00015781862,0.000011044318,0.0013796767,0.7387374,0.04099403,0.117296666,0.000041198346,0.08678812],"study_design_scores_gemma":[0.0001305366,0.00095175894,0.0016936265,0.000012061152,0.00016777168,0.00007545032,0.0004379895,0.8822901,0.005764395,0.108294964,0.00008241535,0.00009890389],"about_ca_topic_score_codex":0.00028433348,"about_ca_topic_score_gemma":0.000015831916,"teacher_disagreement_score":0.35482833,"about_ca_system_score_codex":0.000113824535,"about_ca_system_score_gemma":0.00018471241,"threshold_uncertainty_score":0.9995916},"labels":[],"label_agreement":null},{"id":"W4315646142","doi":"10.1148/ryai.220056","title":"Hurdles to Artificial Intelligence Deployment: Noise in Schemas and “Gold” Labels","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trillium Health Centre; University of Toronto","funders":"Government of Canada; TD Bank","keywords":"Medicine; Software deployment; Noise (video); Artificial intelligence; Software engineering","score_opus":0.12937189406372826,"score_gpt":0.33248091874423014,"score_spread":0.20310902468050188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315646142","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":[0.9112354,0.00052605564,0.07217726,0.009059221,0.0024920984,0.001237653,0.000033867476,0.0008264095,0.002412041],"genre_scores_gemma":[0.99512255,0.00030295097,0.0010014905,0.0017937896,0.0012823178,0.00014161298,0.000059195914,0.00006414637,0.00023194977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962792,0.000054976877,0.0011018622,0.0011350435,0.00035387557,0.0010750614],"domain_scores_gemma":[0.9985574,0.00034079427,0.00021720958,0.0006131501,0.00018897721,0.00008250761],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010526015,0.00048161455,0.00059906114,0.0012998664,0.00028548314,0.00030224206,0.0008255438,0.00031473473,0.0006416507],"category_scores_gemma":[0.0010986228,0.00047998875,0.000093392795,0.0035447518,0.0005078247,0.0010151366,0.00070666184,0.00045713078,0.00765664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029905053,0.00024084926,0.0061188904,0.00016581194,0.000036088768,0.00017545883,0.0004636543,0.0036935175,0.017441524,0.577419,0.0025882039,0.391358],"study_design_scores_gemma":[0.00014037723,0.0002784453,0.017573623,0.0007874882,0.00017953124,0.00013763833,0.005941968,0.14546116,0.089352384,0.6363311,0.09986108,0.003955242],"about_ca_topic_score_codex":0.00067986955,"about_ca_topic_score_gemma":0.0012200144,"teacher_disagreement_score":0.38740274,"about_ca_system_score_codex":0.000064072636,"about_ca_system_score_gemma":0.00005175132,"threshold_uncertainty_score":0.99976516},"labels":[],"label_agreement":null},{"id":"W4319593973","doi":"10.1148/ryai.220170","title":"An Artificial Intelligence Training Workshop for Diagnostic Radiology Residents","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":26,"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","funders":"Queen's University","keywords":"Likert scale; Medical education; Curriculum; Radiology; Artificial intelligence; Competence (human resources); Medicine; Psychology; Computer science; Pedagogy","score_opus":0.34974687223464646,"score_gpt":0.4777457117416867,"score_spread":0.12799883950704022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319593973","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":[0.71356124,0.0005409312,0.26564977,0.010974838,0.0055317497,0.0025077837,0.000051716874,0.00089884026,0.0002831573],"genre_scores_gemma":[0.98927325,0.00067569374,0.003788123,0.0012705743,0.0035246431,0.0007117346,0.00036621987,0.00010713901,0.0002826421],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939885,0.0004538989,0.0020479616,0.0014155474,0.00038617256,0.0017079265],"domain_scores_gemma":[0.99075645,0.006601739,0.00035757606,0.0010301047,0.0005781607,0.00067599304],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026067754,0.00054539915,0.0010002324,0.0008575998,0.0008092122,0.00010304776,0.0006737845,0.0008384393,0.00063426176],"category_scores_gemma":[0.010337281,0.00055402657,0.00033565648,0.0017001258,0.00093394174,0.00034241469,0.00007593098,0.0008259297,0.002071245],"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":[0.0015466121,0.00053206616,0.0027301323,0.00017102789,0.00015602459,0.000205026,0.014051232,0.0071428125,0.0120908795,0.09588782,0.0032224376,0.8622639],"study_design_scores_gemma":[0.00005259824,0.003981227,0.004073031,0.0004033599,0.0003657129,0.00096321804,0.049122956,0.12684107,0.14700031,0.66360885,0.002058092,0.0015295671],"about_ca_topic_score_codex":0.00025254933,"about_ca_topic_score_gemma":0.0004721169,"teacher_disagreement_score":0.86073434,"about_ca_system_score_codex":0.00026410734,"about_ca_system_score_gemma":0.00070002454,"threshold_uncertainty_score":0.9996911},"labels":[],"label_agreement":null},{"id":"W4367848714","doi":"10.1148/ryai.230001","title":"Augmentation of the RSNA Pulmonary Embolism CT Dataset with Bounding Box Annotations and Anatomic Localization of Pulmonary Emboli","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":6,"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":"Medicine; Pulmonary embolism; Nuclear medicine; Internal medicine","score_opus":0.03354814680634203,"score_gpt":0.31032542255382534,"score_spread":0.2767772757474833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367848714","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":[0.9843111,0.00030761212,0.012310933,0.0013242769,0.00031869422,0.00078731263,0.00022030038,0.000047070927,0.00037270074],"genre_scores_gemma":[0.9983745,0.00060083286,0.00015630115,0.00021597726,0.000057037683,0.000037740436,0.0005099741,0.000015549182,0.000032077758],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987793,0.000104552695,0.00045055425,0.00027486816,0.00018280825,0.00020792654],"domain_scores_gemma":[0.99920267,0.00013532628,0.00021774313,0.0003128232,0.00007881873,0.00005263402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033221015,0.00012985861,0.00035318593,0.0002141302,0.00017503914,0.0000094442,0.00013167075,0.000051589614,0.000060683313],"category_scores_gemma":[0.000088503846,0.000097242344,0.000043659464,0.0006709941,0.0004717165,0.00010546564,0.00008260636,0.00010614144,0.000015969581],"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":[0.001649983,0.003435885,0.202735,0.002558726,0.0016095401,0.00067105435,0.010838578,0.06067689,0.11897788,0.23735525,0.018252352,0.34123886],"study_design_scores_gemma":[0.0004426416,0.00096180337,0.5169653,0.00094573677,0.0014576715,0.0008494701,0.013183594,0.29273513,0.13900155,0.028055303,0.0046345117,0.0007672358],"about_ca_topic_score_codex":0.00017659404,"about_ca_topic_score_gemma":0.000049514467,"teacher_disagreement_score":0.34047163,"about_ca_system_score_codex":0.00003430089,"about_ca_system_score_gemma":0.00006750213,"threshold_uncertainty_score":0.3965428},"labels":[],"label_agreement":null},{"id":"W4377989430","doi":"10.1148/ryai.220232","title":"A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging","year":2023,"lang":"en","type":"review","venue":"Radiology Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":207,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Society of Nuclear Medicine and Molecular Imaging","keywords":"Hyperparameter; Convolutional neural network; Artificial intelligence; Machine learning; Field (mathematics); Artificial neural network; Selection (genetic algorithm); Medicine; Computer science","score_opus":0.10014074641117186,"score_gpt":0.4829447736101836,"score_spread":0.38280402719901174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377989430","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006206585,0.49961552,0.4889828,0.0039716163,0.00309086,0.0033764285,0.000050254574,0.00034707383,0.0005033814],"genre_scores_gemma":[0.00024110645,0.984997,0.0077819703,0.001139873,0.0032109474,0.001082319,0.00039864535,0.00026489262,0.00088327186],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99300414,0.0004260108,0.0032070517,0.0014889808,0.00067103404,0.0012028044],"domain_scores_gemma":[0.9952793,0.002570184,0.00048055963,0.00074267236,0.00025488692,0.0006723619],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0048467014,0.0007153359,0.002696137,0.001286846,0.00023064933,0.00013675544,0.00092971977,0.0008400144,0.00049621],"category_scores_gemma":[0.01658091,0.0006418227,0.00071087736,0.0015594783,0.0006416354,0.00010599176,0.00026940447,0.0018982849,0.0014482078],"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":[0.0000996504,0.00011960443,0.000023849887,0.0028780038,0.00009559378,0.00043271005,0.00016451176,0.00031238978,0.000008816275,0.013640197,0.0010804654,0.9811442],"study_design_scores_gemma":[0.00007127442,0.00028028947,0.00000876888,0.014085573,0.000512269,0.0012401727,0.0001864769,0.07954095,0.00014061571,0.015059906,0.88785976,0.0010139614],"about_ca_topic_score_codex":0.00017697722,"about_ca_topic_score_gemma":0.00007053543,"teacher_disagreement_score":0.98013026,"about_ca_system_score_codex":0.0004918005,"about_ca_system_score_gemma":0.0012322798,"threshold_uncertainty_score":0.99960333},"labels":[],"label_agreement":null},{"id":"W4384070440","doi":"10.1148/ryai.220270","title":"The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal University Hospital; Vector Institute; York University; Kingston Health Sciences Centre; Queen's University; Trillium Health Centre; University of Toronto","funders":"","keywords":"Medicine; Chest radiograph; Classifier (UML); Subgroup analysis; Radiography; Generalization; Radiology; Pathology; Artificial intelligence; Meta-analysis","score_opus":0.03459874656786698,"score_gpt":0.32784314266619047,"score_spread":0.2932443960983235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384070440","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":[0.973538,0.0016587225,0.0018123296,0.021437824,0.0007148374,0.00060718285,0.0000090821995,0.00017166346,0.000050357845],"genre_scores_gemma":[0.99115664,0.0040108743,0.0002956137,0.003953333,0.00023940411,0.00015446653,0.00005862831,0.000035026616,0.00009603334],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99747705,0.0004854045,0.00065434095,0.00061496056,0.00016473101,0.0006034878],"domain_scores_gemma":[0.99793655,0.0012783776,0.00014941033,0.0004034864,0.000108856315,0.0001233245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010482967,0.00024120822,0.00037866837,0.00043302344,0.00037682176,0.000049359496,0.00016253217,0.0002864559,0.000051738367],"category_scores_gemma":[0.0011401796,0.00019135932,0.000090285124,0.0012340347,0.0008233119,0.00008798255,0.000095438445,0.0004201405,0.00010262725],"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":[0.0011514273,0.0006189597,0.36932164,0.00022299247,0.00024035785,0.0024240536,0.01787168,0.0035286564,0.14907588,0.093973234,0.035629865,0.32594123],"study_design_scores_gemma":[0.00091289746,0.0027506268,0.6796478,0.00037446935,0.00025913373,0.0020275847,0.0056935097,0.10755051,0.0886321,0.03963395,0.070918106,0.0015992892],"about_ca_topic_score_codex":0.00010330035,"about_ca_topic_score_gemma":0.00048564127,"teacher_disagreement_score":0.32434195,"about_ca_system_score_codex":0.000116250434,"about_ca_system_score_gemma":0.00010564774,"threshold_uncertainty_score":0.7803407},"labels":[],"label_agreement":null},{"id":"W4386285703","doi":"10.1148/ryai.230034","title":"The RSNA Cervical Spine Fracture CT Dataset","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston Health Sciences Centre; Queen's University; University of Toronto; St. Michael's Hospital","funders":"Genentech; National Institutes of Health; National Cancer Institute; Radiological Society of North America","keywords":"Cervical spine; Medicine; Nuclear medicine; Fracture (geology); Radiology; Geology; Surgery; Geotechnical engineering","score_opus":0.03080931013488404,"score_gpt":0.3007360930376755,"score_spread":0.2699267829027915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386285703","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":[0.22106087,0.0068897367,0.69312793,0.05869504,0.008871441,0.000730565,0.00074677874,0.0050028446,0.0048748036],"genre_scores_gemma":[0.9972737,0.0007940288,0.000144119,0.0006123028,0.00052603433,0.00001806281,0.00040537486,0.000020731632,0.00020568211],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988998,0.00006771463,0.00030153763,0.00020431151,0.00013017126,0.0003964438],"domain_scores_gemma":[0.99916863,0.00031977252,0.00002254202,0.00036050956,0.000017805867,0.00011074481],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045468326,0.00013099829,0.00018092974,0.000068102396,0.00021392782,0.000039180788,0.00038279293,0.00006389669,0.00040873545],"category_scores_gemma":[0.0002932617,0.00008928043,0.00006487216,0.00047929646,0.00025249738,0.00004436278,0.000044060263,0.0003805542,0.0025511167],"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":[0.000011757373,0.000034681783,0.00051621784,0.000044469045,0.0002638008,0.00034957074,0.00027564293,0.09879998,0.0030221122,0.011360863,0.5302706,0.3550503],"study_design_scores_gemma":[0.0000150303085,0.00002170479,0.00056270405,0.000012266516,0.00003764624,0.000050558432,0.00020322402,0.6058756,0.0070529305,0.010873803,0.3750702,0.00022431984],"about_ca_topic_score_codex":0.000020110547,"about_ca_topic_score_gemma":0.0000243577,"teacher_disagreement_score":0.7762128,"about_ca_system_score_codex":0.000018631492,"about_ca_system_score_gemma":0.0000123867585,"threshold_uncertainty_score":0.9982255},"labels":[],"label_agreement":null},{"id":"W4388697137","doi":"10.1148/ryai.230132","title":"A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Congenital Heart Disease Studies","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"UK Research and Innovation","keywords":"Intraclass correlation; Medicine; Ejection fraction; Stroke volume; Ventricle; Nuclear medicine; Magnetic resonance imaging; Cardiology; Segmentation; Internal medicine; Radiology; Artificial intelligence; Heart failure; Computer science","score_opus":0.02781489588658895,"score_gpt":0.29575141453650217,"score_spread":0.2679365186499132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388697137","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":[0.9777147,0.0012206527,0.019800603,0.0001803323,0.00031849503,0.0005568969,0.000053654185,0.000110363595,0.000044294546],"genre_scores_gemma":[0.9982903,0.000043279124,0.00093783776,0.000035026835,0.0002758212,0.00006367461,0.0002336946,0.000026827207,0.00009355499],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984856,0.00012135846,0.0004121765,0.00042055213,0.00016181609,0.0003984644],"domain_scores_gemma":[0.99857104,0.0005893028,0.00015445748,0.00027068693,0.00031875452,0.00009574156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017141235,0.00017530307,0.00056473585,0.00013791453,0.00016907933,0.000010862188,0.00010068606,0.00012181606,0.000058830807],"category_scores_gemma":[0.000832581,0.00015349354,0.00017098874,0.0004910494,0.00032430142,0.00006609813,0.000068461384,0.00018121385,0.000060235965],"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":[0.004863227,0.0015770365,0.47222525,0.0004930046,0.001278784,0.00025286764,0.0020687564,0.003992449,0.039052226,0.0004442616,0.0014941347,0.472258],"study_design_scores_gemma":[0.0015488713,0.003548815,0.57369536,0.0005341343,0.0019203469,0.000026537995,0.0077590286,0.31962872,0.07487401,0.0066240923,0.008578268,0.0012617863],"about_ca_topic_score_codex":0.000045768444,"about_ca_topic_score_gemma":0.000014104731,"teacher_disagreement_score":0.47099623,"about_ca_system_score_codex":0.00006397756,"about_ca_system_score_gemma":0.0000706924,"threshold_uncertainty_score":0.6259285},"labels":[],"label_agreement":null},{"id":"W4389136097","doi":"10.1148/ryai.230400","title":"Don’t Forget the Kids!: Novel Pulmonary MRI and AI of Neonatal Lung Disease","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","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":"Western University","funders":"","keywords":"Medicine; Lung; Lung disease; Disease; Pulmonary disease; Intensive care medicine; Pathology; Internal medicine","score_opus":0.029439255534811228,"score_gpt":0.31920559140574756,"score_spread":0.2897663358709363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389136097","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":[0.37658602,0.00028347145,0.618604,0.0025734769,0.0002917617,0.00039813685,0.00020280323,0.00005064444,0.0010097038],"genre_scores_gemma":[0.99919766,0.000032989203,0.000050581595,0.000068156456,0.00026654926,0.00003629191,0.00004398075,0.000014041983,0.0002897491],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988632,0.000071624156,0.00028532048,0.00028928704,0.00013177392,0.00035878818],"domain_scores_gemma":[0.99912417,0.00034610298,0.00006772998,0.00028525226,0.000056915764,0.00011981582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003237899,0.00013572857,0.00020391299,0.00006746254,0.00020621785,0.000018708717,0.00032147288,0.00004359189,0.00014887472],"category_scores_gemma":[0.000024539895,0.00010279154,0.00009120438,0.00030008928,0.00059339596,0.00010100534,0.0001858585,0.0002625643,0.00006565396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020643923,0.00011610175,0.044761777,0.00003207411,0.0001334681,0.000016933973,0.001073059,0.0017562564,0.0054245484,0.60984105,0.0022687248,0.3343696],"study_design_scores_gemma":[0.000048246202,0.000016406939,0.0029231538,0.000018064997,0.00003127048,0.0000053435533,0.00212669,0.7586906,0.0070454213,0.22704503,0.0018223494,0.0002274302],"about_ca_topic_score_codex":0.000113962815,"about_ca_topic_score_gemma":0.0000038177755,"teacher_disagreement_score":0.75693434,"about_ca_system_score_codex":0.000014330516,"about_ca_system_score_gemma":0.00015039447,"threshold_uncertainty_score":0.41917172},"labels":[],"label_agreement":null},{"id":"W4389136100","doi":"10.1148/ryai.230006","title":"Data Liberation and Crowdsourcing in Medical Research: The Intersection of Collective and Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"University of Toronto","keywords":"Crowdsourcing; Bridging (networking); Context (archaeology); Data science; Pace; Computer science; Intersection (aeronautics); Artificial intelligence; Engineering","score_opus":0.531821646880692,"score_gpt":0.5223913049613226,"score_spread":0.009430341919369423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389136100","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":[0.9693726,0.0005116749,0.014230407,0.013905607,0.0007019605,0.000817118,0.000013614696,0.00006205391,0.0003849888],"genre_scores_gemma":[0.9980522,0.0008354409,0.00025789771,0.0002013141,0.00045780354,0.000058941896,0.000041770138,0.000018631583,0.00007601375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658287,0.0006914212,0.0010769182,0.00066858076,0.0004734778,0.0005067639],"domain_scores_gemma":[0.9955543,0.0032051664,0.00014480279,0.0005783683,0.00033546094,0.00018184578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064427443,0.00016965925,0.00037884313,0.0005506838,0.00040331113,0.000047576792,0.0003465493,0.00033585384,0.00016126451],"category_scores_gemma":[0.00682885,0.00013751513,0.000034941888,0.0019210023,0.0015263709,0.00020725765,0.00031024392,0.0009329201,0.00007650353],"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":[0.00079130003,0.00020127604,0.006749475,0.00013054487,0.000047100173,0.000061089755,0.019457385,0.0002685997,0.0037062988,0.047895703,0.0006795764,0.92001164],"study_design_scores_gemma":[0.000030166439,0.0013285144,0.014751237,0.0005815903,0.00006111396,0.00055897934,0.062144294,0.48348156,0.06951858,0.3665967,0.000584488,0.00036280253],"about_ca_topic_score_codex":0.0011952057,"about_ca_topic_score_gemma":0.0038251856,"teacher_disagreement_score":0.9196488,"about_ca_system_score_codex":0.00016126064,"about_ca_system_score_gemma":0.0006570146,"threshold_uncertainty_score":0.81752646},"labels":[],"label_agreement":null},{"id":"W4390536080","doi":"10.1148/ryai.230256","title":"Performance of the Winning Algorithms of the RSNA 2022 Cervical Spine Fracture Detection Challenge","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","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":"University of Toronto","funders":"","keywords":"Receiver operating characteristic; Medicine; Cervical spine; Algorithm; Radiological weapon; Area under curve; Machine learning; Surgery; Nuclear medicine; Internal medicine; Mathematics; Computer science","score_opus":0.01673694940268055,"score_gpt":0.2524782608664479,"score_spread":0.23574131146376737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390536080","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":[0.88139296,0.004152992,0.10937036,0.0018138862,0.002410923,0.00013282457,0.000005479113,0.00012509339,0.00059547846],"genre_scores_gemma":[0.999259,0.0002991121,0.000088163986,0.000045190096,0.00023865815,0.0000059285744,7.271049e-7,0.000011128707,0.00005211758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991478,0.000065421606,0.0003239435,0.000148367,0.0001505263,0.00016399672],"domain_scores_gemma":[0.9995633,0.00008684061,0.000040108258,0.00024950507,0.0000314721,0.000028779192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027990804,0.00010225088,0.00018123299,0.00005481476,0.00007512732,0.0000069194193,0.00028995678,0.000112497015,0.00016873312],"category_scores_gemma":[0.00008693173,0.000059583555,0.00013341192,0.00045184736,0.000260531,0.00004407706,0.000042856485,0.0004727344,0.000015227908],"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":[0.000012527214,0.000055740096,0.0013955692,0.00052188104,0.0002756896,0.0000064637857,0.0021465532,0.1722706,0.09241852,0.0016045076,0.0003093617,0.72898257],"study_design_scores_gemma":[0.0000061436826,0.000029106095,0.0011076677,0.000097513366,0.00004168858,0.000013520038,0.00008343422,0.8149006,0.18209484,0.0005871025,0.0009731179,0.000065274144],"about_ca_topic_score_codex":0.000017112594,"about_ca_topic_score_gemma":0.000011068644,"teacher_disagreement_score":0.7289173,"about_ca_system_score_codex":0.000026010175,"about_ca_system_score_gemma":0.000018778414,"threshold_uncertainty_score":0.2429747},"labels":[],"label_agreement":null},{"id":"W4390703965","doi":"10.1148/ryai.230088","title":"Vision Transformer–based Decision Support for Neurosurgical Intervention in Acute Traumatic Brain Injury: Automated Surgical Intervention Support Tool","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; University of Toronto; St. Michael's Hospital","funders":"St. Michael's Hospital Foundation","keywords":"Traumatic brain injury; Intervention (counseling); Decision support system; Medicine; Medical emergency; Physical medicine and rehabilitation; Physical therapy; Computer science; Nursing; Artificial intelligence; Psychiatry","score_opus":0.04336839606575149,"score_gpt":0.38343532669900426,"score_spread":0.3400669306332528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390703965","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":[0.8016537,0.0003065484,0.19041668,0.0029229964,0.001875573,0.0018469673,0.00009703976,0.00069230347,0.00018821271],"genre_scores_gemma":[0.9970444,0.00006862456,0.0015060971,0.00038107307,0.00014423084,0.00027568993,0.00030838887,0.000051879004,0.00021963757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960925,0.00036883235,0.0017270108,0.0008851094,0.00035130672,0.00057521724],"domain_scores_gemma":[0.99793744,0.0013173225,0.00013047202,0.00037885236,0.00008262995,0.00015326036],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022345893,0.00037393137,0.00077790266,0.0005588491,0.000117014446,0.00010311668,0.0002494102,0.00038656162,0.0012979788],"category_scores_gemma":[0.00041357413,0.00032067575,0.000920216,0.00068849634,0.00031311068,0.00032024033,0.000023134276,0.00053334085,0.0002172997],"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":[0.009292239,0.0019717175,0.0003197174,0.0012802525,0.00029982047,0.0014879397,0.0007008954,0.0002700825,0.007869973,0.010948414,0.0057051564,0.95985377],"study_design_scores_gemma":[0.0037218637,0.023718068,0.012979843,0.0037926866,0.0010280557,0.003950371,0.00045939907,0.8026776,0.09252325,0.016805734,0.036889695,0.0014534722],"about_ca_topic_score_codex":0.000011297846,"about_ca_topic_score_gemma":0.00004264299,"teacher_disagreement_score":0.9584003,"about_ca_system_score_codex":0.00013311929,"about_ca_system_score_gemma":0.00012488243,"threshold_uncertainty_score":0.99992454},"labels":[],"label_agreement":null},{"id":"W4390704971","doi":"10.1148/ryai.230327","title":"AI for Detection of Tuberculosis: Implications for Global Health","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":29,"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 Montréal; Impact","funders":"Bundesministerium für Bildung und Forschung","keywords":"Tuberculosis; Medicine; CAD; Global health; Checklist; Triage; Medical physics; Public health; Pathology; Medical emergency; Engineering; Psychology","score_opus":0.07560498107747346,"score_gpt":0.4404564319504423,"score_spread":0.3648514508729689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390704971","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":[0.008715631,0.0021871133,0.8336114,0.15314353,0.00082598685,0.0012082149,0.00015204237,0.00014412457,0.000011938819],"genre_scores_gemma":[0.9868604,0.00015474706,0.0025792026,0.00942598,0.00040330188,0.00050266815,0.000033692442,0.000018345421,0.000021661412],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987514,0.000041209067,0.0004914413,0.00038162802,0.000054366046,0.00027997023],"domain_scores_gemma":[0.99865884,0.00074004306,0.00007135291,0.00025010217,0.00018854091,0.000091098904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044911494,0.000116721356,0.00030989453,0.000112321686,0.00011986534,0.00001465132,0.00009170648,0.0001363444,0.000015075899],"category_scores_gemma":[0.00057839084,0.00011309475,0.00019293407,0.0003594015,0.00015487612,0.00004860404,0.000015064888,0.00010395342,0.000018230152],"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":[0.00034971363,0.00023554168,0.00037019947,0.00087249785,0.00019064287,0.0000011678535,0.00033830525,0.00060483976,0.037785776,0.21529679,0.010776332,0.7331782],"study_design_scores_gemma":[0.00023583895,0.004815302,0.007328557,0.0006867593,0.00058403594,0.00033990387,0.00025600952,0.09655111,0.2942558,0.4395453,0.15492377,0.00047762325],"about_ca_topic_score_codex":0.000089599125,"about_ca_topic_score_gemma":0.00016972407,"teacher_disagreement_score":0.97814476,"about_ca_system_score_codex":0.0003578276,"about_ca_system_score_gemma":0.00045326797,"threshold_uncertainty_score":0.461187},"labels":[],"label_agreement":null},{"id":"W4391109191","doi":"10.1148/ryai.230513","title":"Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Western University","funders":"","keywords":"Medicine; Medical physicist; Clinical Practice; Purchasing; Radiology; Medical physics; Artificial intelligence; Computer science; Engineering; Operations management; Family medicine","score_opus":0.365203840080182,"score_gpt":0.5070261272877337,"score_spread":0.1418222872075517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391109191","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":[0.86822194,0.0075230408,0.07579887,0.04603299,0.0013268093,0.0009560773,0.000020385072,0.00009354719,0.000026347008],"genre_scores_gemma":[0.97474915,0.0030256717,0.019738285,0.0015747614,0.00070302916,0.00012926618,0.00002970883,0.00002361971,0.000026504607],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99726707,0.00032560423,0.0010183101,0.00065957283,0.00015141409,0.0005780556],"domain_scores_gemma":[0.9962702,0.0030839036,0.00011101119,0.00025012722,0.00014800938,0.00013677777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017679034,0.00024174887,0.00038922363,0.00010184439,0.0004655809,0.00023427977,0.00006809446,0.00025913143,0.00012316582],"category_scores_gemma":[0.001670482,0.00019250804,0.00007090822,0.00026386304,0.00052633544,0.00029224931,0.00010052716,0.00078778784,0.000032439377],"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":[0.00037968584,0.00028924123,0.16864622,0.00035915294,0.00058097596,0.0005060466,0.092993036,0.00018463623,0.017388977,0.117041335,0.0033879578,0.59824276],"study_design_scores_gemma":[0.00048322466,0.0016733004,0.07147597,0.0028803712,0.0009044624,0.0055104964,0.16671982,0.21975112,0.22591221,0.2505248,0.051667966,0.0024962449],"about_ca_topic_score_codex":0.0017643893,"about_ca_topic_score_gemma":0.0007956249,"teacher_disagreement_score":0.5957465,"about_ca_system_score_codex":0.00025165873,"about_ca_system_score_gemma":0.0006314108,"threshold_uncertainty_score":0.78502506},"labels":[],"label_agreement":null},{"id":"W4392748966","doi":"10.1148/ryai.230079","title":"Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":12,"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":"Hamilton Health Sciences; Google","keywords":"Medicine; Retrospective cohort study; Receiver operating characteristic; Lung cancer; Workflow; Medical physics; Lung cancer screening; Multinational corporation; Artificial intelligence; General surgery; Surgery; Pathology; Internal medicine; Computer science; Database","score_opus":0.04008904032462744,"score_gpt":0.38428533918854296,"score_spread":0.3441962988639155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392748966","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":[0.9887173,0.0018304457,0.0007190653,0.0076313578,0.00016222984,0.0008213091,0.000018245002,0.000025447214,0.000074556345],"genre_scores_gemma":[0.99835485,0.00045243127,0.00006336762,0.0005862287,0.000103668266,0.00037255252,0.000026384,0.00000954729,0.000030966057],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99883235,0.0001779467,0.0002727747,0.00037470442,0.00014052236,0.00020172576],"domain_scores_gemma":[0.9992533,0.00049614045,0.000030431698,0.00011654114,0.00006692638,0.00003671311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046052004,0.0001303306,0.00022763106,0.00027926726,0.00006320366,0.00003340655,0.000070058464,0.00006390347,0.00008189469],"category_scores_gemma":[0.00011668089,0.000087598906,0.000031541465,0.00064676034,0.00016097425,0.000054789914,0.00002434817,0.0003453681,0.0000055031755],"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":[0.0001968571,0.000508478,0.9709004,0.000030245963,0.0001700471,0.0002699696,0.011528601,0.00082185114,0.00002695056,0.0057798126,0.0003371438,0.009429655],"study_design_scores_gemma":[0.00015656388,0.00045816216,0.92126834,0.0001922712,0.00009057991,0.00002847187,0.004526658,0.071722925,0.00018596229,0.0011534307,0.00012584445,0.00009078305],"about_ca_topic_score_codex":0.0034343814,"about_ca_topic_score_gemma":0.005607856,"teacher_disagreement_score":0.07090107,"about_ca_system_score_codex":0.00036350384,"about_ca_system_score_gemma":0.00008312828,"threshold_uncertainty_score":0.51917803},"labels":[],"label_agreement":null},{"id":"W4399873374","doi":"10.1148/ryai.240263","title":"Navigating Clinical Variability: Transfer Learning’s Impact on Imaging Model Performance","year":2024,"lang":"en","type":"letter","venue":"Radiology Artificial Intelligence","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré de Santé et de Services Sociaux des Laurentides","funders":"","keywords":"Transfer of learning; Computer science; Artificial intelligence","score_opus":0.06518302432630502,"score_gpt":0.3727762012536321,"score_spread":0.30759317692732707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399873374","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":[0.01147947,0.00017849453,0.8396998,0.14155304,0.0053261146,0.00039368216,0.000013789637,0.00073995616,0.0006156614],"genre_scores_gemma":[0.8190873,0.00032920684,0.006259672,0.16521546,0.008340332,0.00014836362,0.000042721862,0.00015643737,0.00042044793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939683,0.0010525313,0.0014502174,0.0020145327,0.00049844256,0.0010159527],"domain_scores_gemma":[0.9966175,0.0016381163,0.00021045144,0.0012335458,0.00017287422,0.00012751854],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030735554,0.0006921061,0.0008544541,0.00016726556,0.00035765933,0.00029186264,0.0018310136,0.001233315,0.00008452224],"category_scores_gemma":[0.00033875625,0.00060887606,0.0006046962,0.0006015697,0.0005673044,0.00043075767,0.00024662027,0.011917777,0.0011716844],"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":[0.00007904953,0.00005871218,0.00047710355,0.00017091252,0.00016370388,0.00030621252,0.0010140088,0.2162859,0.000078268255,0.0029369018,0.07998127,0.69844794],"study_design_scores_gemma":[0.000019273426,0.00048023448,0.000028171722,0.00026002093,0.00004890758,0.00020204358,0.000009017208,0.9639894,0.0009903784,0.022464223,0.010902816,0.0006055445],"about_ca_topic_score_codex":0.00003470621,"about_ca_topic_score_gemma":0.0000020540222,"teacher_disagreement_score":0.8334401,"about_ca_system_score_codex":0.0005895923,"about_ca_system_score_gemma":0.00047115938,"threshold_uncertainty_score":0.99963623},"labels":[],"label_agreement":null},{"id":"W4401953142","doi":"10.1148/ryai.230296","title":"Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical 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":"Science North","funders":"National Institute of Neurological Disorders and Stroke; Northwestern Memorial Foundation; Northwestern University","keywords":"Head (geology); Artificial intelligence; Medicine; Radiology; Deep learning; Computed tomography; Computer science; Computer vision; Geology","score_opus":0.07135883899809214,"score_gpt":0.33877764259056603,"score_spread":0.2674188035924739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401953142","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":[0.3905134,0.00013077965,0.60656893,0.0007911488,0.00029068667,0.0005265921,0.000011970824,0.00013463676,0.0010318687],"genre_scores_gemma":[0.9927356,0.000055104327,0.0060279514,0.00031183084,0.00035287283,0.000038566333,0.000036912545,0.00006771327,0.00037343107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974212,0.00031535368,0.0006916451,0.0006929483,0.00042707162,0.00045179884],"domain_scores_gemma":[0.998601,0.0005895542,0.00013167424,0.00033493922,0.00015370203,0.00018912634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00097604893,0.00032750424,0.00062125793,0.00035478533,0.00018351914,0.000050390725,0.00021335436,0.000117503136,0.00020450604],"category_scores_gemma":[0.0007666237,0.00025563277,0.00010472033,0.00059266324,0.00045333005,0.00013330895,0.000033269112,0.0011326617,0.00007986661],"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":[0.0018966874,0.0010001699,0.0008433769,0.00022289176,0.00023826824,0.0021343976,0.0030061489,0.29286215,0.0054241926,0.010387962,0.000075230164,0.68190855],"study_design_scores_gemma":[0.00031567106,0.0034714246,0.00033829483,0.00040122058,0.00016830923,0.0005791288,0.00097399496,0.98472893,0.006343111,0.0023204547,0.000099595105,0.00025984403],"about_ca_topic_score_codex":0.00013327804,"about_ca_topic_score_gemma":0.00006211463,"teacher_disagreement_score":0.6918668,"about_ca_system_score_codex":0.00012210681,"about_ca_system_score_gemma":0.00016773386,"threshold_uncertainty_score":0.99998957},"labels":[],"label_agreement":null},{"id":"W4402601437","doi":"10.1148/ryai.230550","title":"Assessing the Performance of Models from the 2022 RSNA Cervical Spine Fracture Detection Competition at a Level I Trauma Center","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","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 Toronto","funders":"St. Michael's Hospital Foundation","keywords":"Cervical spine; Center (category theory); Trauma center; Medicine; Competition (biology); Internal medicine; Surgery; Retrospective cohort study","score_opus":0.05115680240699588,"score_gpt":0.2940749643982224,"score_spread":0.2429181619912265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402601437","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":[0.56828,0.0010682843,0.4290023,0.00089578814,0.0004792403,0.00005047497,0.000012517699,0.000074213174,0.00013718213],"genre_scores_gemma":[0.9989496,0.00022881656,0.00017921657,0.00018954747,0.00037688052,0.000010778306,0.000024864723,0.000013737531,0.00002654305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990473,0.00010549838,0.00031980852,0.00019175118,0.00014847596,0.00018716606],"domain_scores_gemma":[0.9994077,0.0002895053,0.000031861662,0.00020558166,0.000028911238,0.000036405465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032459284,0.00012322947,0.00017251208,0.000043719905,0.00016541586,0.000052645937,0.00021180639,0.00010609405,0.00041906975],"category_scores_gemma":[0.000033720164,0.000072838186,0.00009997132,0.00023699914,0.000216193,0.00015418576,0.00002972118,0.0004581547,0.000059824037],"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":[0.000022430926,0.000058166737,0.00070360635,0.00012407586,0.00031459978,0.000016386446,0.002613578,0.46081337,0.06229382,0.0022590877,0.00072268926,0.47005817],"study_design_scores_gemma":[0.000010504775,0.000017060243,0.0012005904,0.000070958224,0.000044932767,0.000020125632,0.0001840838,0.94503486,0.05066875,0.0018748681,0.00079391047,0.00007936702],"about_ca_topic_score_codex":0.000060512237,"about_ca_topic_score_gemma":0.00006501253,"teacher_disagreement_score":0.48422146,"about_ca_system_score_codex":0.000058488196,"about_ca_system_score_gemma":0.000010699331,"threshold_uncertainty_score":0.458852},"labels":[],"label_agreement":null},{"id":"W4403683884","doi":"10.1148/ryai.240101","title":"The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Pelvic and Acetabular Injuries","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital","funders":"National Cancer Institute","keywords":"Medicine; Radiology","score_opus":0.05125619236217202,"score_gpt":0.36198951248450617,"score_spread":0.3107333201223341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403683884","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":[0.80405354,0.037217226,0.09462142,0.043060448,0.008515733,0.0022590975,0.0013636202,0.0008831162,0.008025794],"genre_scores_gemma":[0.9966002,0.00050415547,0.0005565341,0.00073750946,0.0006049426,0.0000420635,0.0001736284,0.00002137428,0.00075956766],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984627,0.00012528077,0.0005037372,0.00034630188,0.0001706015,0.00039140703],"domain_scores_gemma":[0.9987639,0.0005712452,0.000047744765,0.00047127248,0.000034967128,0.00011082475],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007082467,0.00018505377,0.00029819214,0.00008636945,0.0003034066,0.000092767405,0.00025547232,0.00007617398,0.0006580389],"category_scores_gemma":[0.00038088326,0.00011678458,0.00010804686,0.00025228303,0.0007892957,0.000102283666,0.000051639363,0.00043682006,0.0020605791],"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":[0.00064448564,0.00014887987,0.00017482782,0.00019577352,0.00035886455,0.0008706475,0.0010732679,0.000025629499,0.0105552655,0.19077706,0.15552762,0.63964766],"study_design_scores_gemma":[0.00011118091,0.0015122348,0.0007957216,0.0004842069,0.0007253816,0.0052615176,0.003508227,0.04661552,0.11655046,0.09386344,0.7297542,0.000817898],"about_ca_topic_score_codex":0.000025660425,"about_ca_topic_score_gemma":0.000041474235,"teacher_disagreement_score":0.63882977,"about_ca_system_score_codex":0.00004739947,"about_ca_system_score_gemma":0.00016562252,"threshold_uncertainty_score":0.9987164},"labels":[],"label_agreement":null},{"id":"W4404107887","doi":"10.1148/ryai.240334","title":"RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Abdominal Trauma and Injuries","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":"Health Sciences Centre; Vancouver General Hospital; North York General Hospital; Sunnybrook Health Science Centre; University of Toronto; St. Michael's Hospital","funders":"St. Michael's Hospital Foundation","keywords":"Medicine; Receiver operating characteristic; Extravasation; Radiology; Artificial intelligence; Nuclear medicine; Internal medicine; Computer science; Pathology","score_opus":0.06355374889793619,"score_gpt":0.38617302412992605,"score_spread":0.32261927523198985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404107887","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12247145,0.6244324,0.018194044,0.20555755,0.002543284,0.0021764184,0.00006455695,0.00065331976,0.023906965],"genre_scores_gemma":[0.96133417,0.029045474,0.000763552,0.005120533,0.0004744047,0.00006770363,0.000011870604,0.000033884095,0.0031484235],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984032,0.00008915795,0.0005395829,0.00048238292,0.00013409961,0.00035157666],"domain_scores_gemma":[0.9991617,0.000299131,0.00004283133,0.00026967027,0.00006809558,0.00015857507],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00049484445,0.00024162495,0.0006816153,0.00013387357,0.00010805711,0.000029912528,0.000119914504,0.00017781068,0.0014674154],"category_scores_gemma":[0.000264071,0.00018330645,0.0001644063,0.00022408858,0.0005600876,0.0001277188,0.00003973588,0.00048341122,0.00093602086],"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":[0.0003141909,0.0001757898,0.0005367372,0.0030221527,0.00036768406,0.00063683663,0.00078627584,0.0000018972001,0.0011445992,0.11489489,0.021559615,0.85655934],"study_design_scores_gemma":[0.0006350683,0.011109308,0.029642574,0.013325697,0.007841564,0.014862789,0.0037680988,0.010532717,0.047734037,0.22138259,0.635335,0.003830517],"about_ca_topic_score_codex":0.000016641212,"about_ca_topic_score_gemma":0.000018438759,"teacher_disagreement_score":0.85272884,"about_ca_system_score_codex":0.00003795324,"about_ca_system_score_gemma":0.00007242699,"threshold_uncertainty_score":0.99984187},"labels":[],"label_agreement":null},{"id":"W4404107931","doi":"10.1148/ryai.240005","title":"SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"National Institute of Neurological Disorders and Stroke; Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; HORIZON EUROPE Framework Programme; Alliance de recherche numérique du Canada; Institut pour la Recherche sur la Moelle épinière et l'Encéphale; Boettcher Foundation; Ministerstvo Zdravotnictví Ceské Republiky; Institut de Valorisation des Données; Eunice Kennedy Shriver National Institute of Child Health and Human Development; European Commission; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Canada First Research Excellence Fund; Canada Research Chairs; Craig H. Neilsen Foundation; Canadian Institutes of Health Research; National Science Foundation","keywords":"Medicine; Lesion; Spinal cord; Intramedullary rod; Magnetic resonance imaging; Sagittal plane; Radiology; Spinal cord injury; Segmentation; Cord; Lumbar; Surgery; Artificial intelligence; Computer science","score_opus":0.03020416481156932,"score_gpt":0.32852467621466286,"score_spread":0.2983205114030935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404107931","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":[0.8129585,0.00090461475,0.18370956,0.00060754566,0.0009432912,0.00014611016,0.000013541951,0.0002820341,0.00043480055],"genre_scores_gemma":[0.99768376,0.00029014907,0.0017857245,0.00006335411,0.00010666229,0.000019383368,0.000013447118,0.000016073545,0.000021466789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877656,0.0000864105,0.0005194793,0.0002310451,0.00014354021,0.00024296711],"domain_scores_gemma":[0.99950886,0.00019459256,0.000029515437,0.00016831362,0.000022111873,0.000076632015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003274202,0.00013987377,0.00026338134,0.00041563844,0.00003818527,0.000021098656,0.00015906805,0.000112219976,0.00036560325],"category_scores_gemma":[0.0000930524,0.00012827388,0.00007273059,0.0006958295,0.00021203193,0.00007142434,0.000015177966,0.00033732218,0.00020378083],"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":[0.000046502268,0.000062105064,0.00015954243,0.00016902885,0.0000702972,0.00006061336,0.0003141685,0.009522492,0.013976126,0.007288631,0.000812409,0.9675181],"study_design_scores_gemma":[0.000015372669,0.00028691703,0.00031946442,0.0004097046,0.0000422121,0.00001596755,0.00024644242,0.935114,0.056046184,0.00721302,0.00012824014,0.00016246422],"about_ca_topic_score_codex":0.000032506956,"about_ca_topic_score_gemma":0.000018712191,"teacher_disagreement_score":0.9673556,"about_ca_system_score_codex":0.000084592226,"about_ca_system_score_gemma":0.000044805336,"threshold_uncertainty_score":0.5230857},"labels":[],"label_agreement":null},{"id":"W4408364279","doi":"10.1148/ryai.240287","title":"External Testing of a Commercial AI Algorithm for Breast Cancer Detection at Screening Mammography","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of Calgary; Kelowna General Hospital","funders":"Mitacs; Canadian Cancer Society","keywords":"Receiver operating characteristic; Medicine; Breast cancer; Mammography; Algorithm; Breast cancer screening; Area under the curve; Area under curve; Retrospective cohort study; Machine learning; Cancer; Artificial intelligence; Internal medicine; Oncology; Mathematics; Computer science","score_opus":0.04239276374823154,"score_gpt":0.32273398706420203,"score_spread":0.2803412233159705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408364279","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":[0.013676197,0.00031462454,0.98291665,0.0006373576,0.0018917233,0.0003313226,0.000035178855,0.00013236063,0.00006455992],"genre_scores_gemma":[0.8409838,0.000026882879,0.15797661,0.00041358278,0.00037112948,0.000171772,9.054535e-7,0.000012068267,0.000043264106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983028,0.000120832396,0.0005319083,0.00052049453,0.00014393654,0.00038003974],"domain_scores_gemma":[0.99852467,0.0005078032,0.00023409302,0.0003466965,0.00033028884,0.000056472378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049013086,0.0001790913,0.00028545858,0.00033446203,0.0003903393,0.000041226285,0.000626276,0.00017050246,0.000024471547],"category_scores_gemma":[0.000090554764,0.00019115998,0.00014080813,0.001057593,0.00026308344,0.00024790288,0.0001948743,0.00023311397,0.00000503224],"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":[0.0000835507,0.0000264477,0.0019821103,0.000018361872,0.000037540063,0.0000016371266,0.00009848984,0.0017810748,0.016574807,0.0028069834,0.000056554112,0.97653246],"study_design_scores_gemma":[0.00007607161,0.00019146099,0.011520179,0.00011021324,0.00003186115,0.00009837576,0.000027606262,0.6804549,0.28779727,0.019189686,0.00028844416,0.00021392191],"about_ca_topic_score_codex":0.0004042326,"about_ca_topic_score_gemma":0.00041576548,"teacher_disagreement_score":0.97631854,"about_ca_system_score_codex":0.00016753518,"about_ca_system_score_gemma":0.00009677906,"threshold_uncertainty_score":0.7795278},"labels":[],"label_agreement":null},{"id":"W4408848825","doi":"10.1148/ryai.250032","title":"Bridging the Trust Gap: Conformal Prediction for AI-based Intracranial Hemorrhage Detection","year":2025,"lang":"en","type":"letter","venue":"Radiology Artificial Intelligence","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","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":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Bridging (networking); Conformal map; Psychology; Computer science; Mathematics; Computer security; Geometry","score_opus":0.04192507697303387,"score_gpt":0.31553091347988066,"score_spread":0.2736058365068468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408848825","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004948351,0.00017939886,0.5865976,0.40267184,0.0024697075,0.0021462652,0.00020544908,0.00019426708,0.00058710214],"genre_scores_gemma":[0.32865486,0.0000669305,0.00403399,0.62275755,0.034708872,0.001295111,0.0015400933,0.00014267342,0.006799916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657905,0.00025614462,0.0009604807,0.0007479818,0.00043535538,0.0010209813],"domain_scores_gemma":[0.99776417,0.000770062,0.00022042477,0.0006482283,0.0004984972,0.000098611454],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0010621303,0.00048011844,0.0007587307,0.0004269128,0.0007265559,0.00010014511,0.0004484926,0.001651826,0.00051724643],"category_scores_gemma":[0.00090962736,0.00035952518,0.0004655711,0.00041739226,0.0006579728,0.00011423249,0.000060263294,0.0041879793,0.00016182051],"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":[0.002151173,0.000102596234,0.00006522086,0.0013155043,0.00017898476,0.000960103,0.00015010043,0.00037012566,0.0026833948,0.00056944136,0.14508535,0.846368],"study_design_scores_gemma":[0.00021304248,0.0008161241,0.000030769905,0.00011215979,0.00035518667,0.0010436731,0.00006926616,0.9225091,0.042270947,0.0015037383,0.030740095,0.00033590014],"about_ca_topic_score_codex":0.00008582325,"about_ca_topic_score_gemma":0.00003746574,"teacher_disagreement_score":0.922139,"about_ca_system_score_codex":0.00032791463,"about_ca_system_score_gemma":0.000670745,"threshold_uncertainty_score":0.9998857},"labels":[],"label_agreement":null},{"id":"W4409108531","doi":"10.1148/ryai.240628","title":"Predicting Respiratory Disease Mortality Risk Using Open-Source AI on Chest Radiographs in an Asian Health Screening Population","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"COVID-19 diagnosis using AI","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Research Foundation of Korea","keywords":"Medicine; Interquartile range; Quartile; Hazard ratio; Population; Retrospective cohort study; Confidence interval; Internal medicine; Environmental health","score_opus":0.1351660084053331,"score_gpt":0.4433733217492758,"score_spread":0.3082073133439427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409108531","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":[0.93923545,0.00040467904,0.051117387,0.007424746,0.00042048993,0.0011675876,0.000022579643,0.00016760372,0.00003945891],"genre_scores_gemma":[0.9798786,0.000037746857,0.0013193886,0.018378485,0.00024689117,0.00004813933,0.00004729694,0.000034893004,0.000008575265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99612886,0.0010861487,0.0010250884,0.0009469753,0.00023914177,0.00057381607],"domain_scores_gemma":[0.9979438,0.0004441146,0.00035100494,0.00081034994,0.00009557898,0.0003551409],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002336114,0.00028896923,0.0006360651,0.0008126257,0.00051395956,0.000090510846,0.00038073983,0.0002125517,0.00003682773],"category_scores_gemma":[0.0017709767,0.0003122725,0.00012639957,0.0012275821,0.00026096602,0.0002842407,0.00013359399,0.0008057264,0.000007464253],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060519174,0.0004074812,0.8782588,0.0000845367,0.000048408645,0.000046528217,0.00047786534,0.03742486,0.00018643877,0.0015744165,0.00010505747,0.08078042],"study_design_scores_gemma":[0.00020153935,0.00041163855,0.86330366,0.0007821352,0.000108358356,0.0000051550865,0.0003356438,0.13118908,0.0005246178,0.0023047447,0.00059701,0.00023644253],"about_ca_topic_score_codex":0.007459585,"about_ca_topic_score_gemma":0.0031074288,"teacher_disagreement_score":0.093764216,"about_ca_system_score_codex":0.00054274383,"about_ca_system_score_gemma":0.0005409929,"threshold_uncertainty_score":0.99993294},"labels":[],"label_agreement":null},{"id":"W4409503838","doi":"10.1148/ryai.240528","title":"The BraTS-Africa Dataset: Expanding the Brain Tumor Segmentation Data to Capture African Populations","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Montreal Neurological Institute and Hospital; Lawson Health Research Institute; McGill University; University of British Columbia","funders":"National Cancer Institute","keywords":"Segmentation; Computer science; Artificial intelligence","score_opus":0.08928174279245121,"score_gpt":0.40137051459818235,"score_spread":0.31208877180573114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409503838","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":[0.045334164,0.0018735868,0.54066867,0.40364817,0.0028931568,0.0017784426,0.00037127384,0.00016357902,0.0032689786],"genre_scores_gemma":[0.98376215,0.000042066185,0.0037524719,0.01013126,0.00040719382,0.000064257685,0.0007033948,0.00001828653,0.0011189202],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983471,0.0002550582,0.0004358761,0.00042605225,0.00017778021,0.00035814187],"domain_scores_gemma":[0.9976889,0.0010624862,0.0000961149,0.0009888307,0.000052102823,0.000111598456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014048953,0.00014593563,0.0002108586,0.0001064378,0.00087118737,0.00009268244,0.0007446517,0.000059415852,0.00008140588],"category_scores_gemma":[0.003428572,0.00008660214,0.000040781666,0.0005559254,0.00037082442,0.00008176918,0.00025337673,0.00053361274,0.000076718265],"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":[0.00042532588,0.00015417431,0.0029201952,0.000038298105,0.00022817458,0.000085267944,0.0041569243,0.0032827172,0.028084377,0.11474447,0.5834493,0.2624308],"study_design_scores_gemma":[0.000262574,0.00030962692,0.008515775,0.00023431575,0.00045317752,0.00041296356,0.015331965,0.24174154,0.0048048836,0.02913942,0.6982046,0.00058916135],"about_ca_topic_score_codex":0.00021584392,"about_ca_topic_score_gemma":0.00023262166,"teacher_disagreement_score":0.938428,"about_ca_system_score_codex":0.0000755856,"about_ca_system_score_gemma":0.000133151,"threshold_uncertainty_score":0.6700557},"labels":[],"label_agreement":null},{"id":"W4409503873","doi":"10.1148/ryai.250125","title":"Beyond Double Reading: Multiple Deep Learning Models Enhancing Radiologist-led Breast Screening","year":2025,"lang":"en","type":"letter","venue":"Radiology Artificial Intelligence","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches","funders":"","keywords":"Reading (process); Computer science; Deep learning; Medicine; Medical physics; Radiology; Artificial intelligence; Linguistics","score_opus":0.05101416837256416,"score_gpt":0.28152450101798093,"score_spread":0.23051033264541676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409503873","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":[0.00008814906,0.0006493811,0.92119884,0.06961906,0.0041845418,0.00060344033,0.000011992969,0.00089792506,0.0027466398],"genre_scores_gemma":[0.38669476,0.0013211629,0.25000423,0.32344395,0.025038878,0.0013557307,0.00063426635,0.0004586455,0.0110483775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99339294,0.0008489845,0.0014329173,0.0022821294,0.0005072854,0.0015357184],"domain_scores_gemma":[0.9952337,0.001969171,0.00078686577,0.0014930732,0.00037171145,0.00014545661],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0014432217,0.00087116484,0.0012064438,0.000830584,0.00094017206,0.00035602457,0.0030030662,0.0021519146,0.00009751633],"category_scores_gemma":[0.00034417753,0.0009360505,0.00040855422,0.0011570172,0.0005511745,0.0008474929,0.0007790273,0.005195661,0.00020606734],"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":[0.00043241042,0.000077277735,0.00016970081,0.0003080117,0.00060577993,0.0009971892,0.0025251533,0.38294622,0.002664692,0.039566446,0.10633963,0.4633675],"study_design_scores_gemma":[0.00014094204,0.0002501845,0.000011471276,0.00025002516,0.00008757089,0.0010162136,0.00013204265,0.89621145,0.013640177,0.05176283,0.03519385,0.0013032277],"about_ca_topic_score_codex":0.00040755665,"about_ca_topic_score_gemma":0.00018437786,"teacher_disagreement_score":0.6711946,"about_ca_system_score_codex":0.00061906874,"about_ca_system_score_gemma":0.0003097409,"threshold_uncertainty_score":0.999309},"labels":[],"label_agreement":null},{"id":"W4413006736","doi":"10.1148/ryai.240478","title":"Automated Deep Learning–based Segmentation of the Dentate Nucleus Using Quantitative Susceptibility Mapping MRI","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Advanced MRI Techniques and Applications","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":"Montreal Neurological Institute and Hospital; McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Health and Medical Research Council; National Institutes of Health; Friedreich's Ataxia Research Alliance","keywords":"Intraclass correlation; Segmentation; Sørensen–Dice coefficient; Artificial intelligence; Deep learning; Dentate nucleus; Computer science; Magnetic resonance imaging; Medicine; Pattern recognition (psychology); Image segmentation; Psychology; Radiology; Cerebellum; Neuroscience","score_opus":0.06647520502677146,"score_gpt":0.40077746015255356,"score_spread":0.3343022551257821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413006736","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":[0.3263081,0.00005976373,0.6724139,0.00041549568,0.00008057401,0.0004328394,0.0000033260492,0.0001292896,0.0001567446],"genre_scores_gemma":[0.9022448,0.00002436901,0.09743272,0.00018528759,0.000013612859,0.000027910794,0.000008447085,0.000008033487,0.00005481235],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890316,0.00014499626,0.0004372161,0.00025739035,0.000083480845,0.00017373764],"domain_scores_gemma":[0.9991278,0.00021336961,0.00019660812,0.00026544995,0.00016700424,0.00002973392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026741906,0.00010939363,0.0002271322,0.00010047675,0.00021065112,0.0000061480105,0.000131128,0.0000970298,0.000072605595],"category_scores_gemma":[0.00024310352,0.00008821062,0.0000927265,0.000570925,0.00042041184,0.00004109299,0.000042109394,0.00022422597,0.000011672156],"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":[0.00026277508,0.00026879035,0.01916985,0.00009901223,0.000054981323,0.0000043448617,0.00088648434,0.16517818,0.75531274,0.0392306,0.000069657195,0.019462612],"study_design_scores_gemma":[0.00005262156,0.000102446895,0.0047675534,0.00008909928,0.000056106463,0.000009229165,0.0013106646,0.6148109,0.3717144,0.0068876804,0.00012252085,0.00007679544],"about_ca_topic_score_codex":0.000054520602,"about_ca_topic_score_gemma":0.000037175312,"teacher_disagreement_score":0.57593673,"about_ca_system_score_codex":0.00012737252,"about_ca_system_score_gemma":0.000094113,"threshold_uncertainty_score":0.35971248},"labels":[],"label_agreement":null},{"id":"W4413006740","doi":"10.1148/ryai.240777","title":"Segmenting Whole-Body MRI and CT for Multiorgan Anatomic Structure Delineation","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Leibniz-Gemeinschaft; European Commission; Bundesministerium für Bildung und Forschung; Wilhelm Sander-Stiftung","keywords":"Medicine; Segmentation; Magnetic resonance imaging; Radiology; Artificial intelligence; Retrospective cohort study; Sørensen–Dice coefficient; Medical physics; Nuclear medicine; Computer science; Surgery; Image segmentation","score_opus":0.014852897436273691,"score_gpt":0.3371446896156106,"score_spread":0.3222917921793369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413006740","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":[0.47181192,0.00035643132,0.5218209,0.00509206,0.00042108094,0.00034552152,0.0000074913282,0.000053035354,0.00009153073],"genre_scores_gemma":[0.98353314,0.000070152295,0.01436248,0.0013033983,0.0002670633,0.000016107178,0.000056873534,0.0000146431585,0.00037615394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998869,0.00005563767,0.0003804203,0.00035661666,0.00007148247,0.0002668307],"domain_scores_gemma":[0.99929994,0.0002735204,0.00008281777,0.00016786743,0.00009447572,0.00008134566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003627051,0.00014097123,0.00028854265,0.00017116408,0.00021075536,0.000028861043,0.0000912519,0.00009197265,0.00004850023],"category_scores_gemma":[0.0008923186,0.00012691761,0.000057702815,0.00017636667,0.00021864858,0.0000496018,0.000035923542,0.00033189723,0.0000070983974],"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":[0.00027809356,0.00008865225,0.02696873,0.00023300062,0.00020668992,0.00003749542,0.0004256404,0.0015765404,0.263247,0.04598583,0.001982471,0.6589698],"study_design_scores_gemma":[0.00033607654,0.0001596092,0.0040782928,0.00014518041,0.00019695425,0.00023288724,0.00047945403,0.8975403,0.07275604,0.013953934,0.0099020405,0.00021927817],"about_ca_topic_score_codex":0.00002153556,"about_ca_topic_score_gemma":0.000010391713,"teacher_disagreement_score":0.8959637,"about_ca_system_score_codex":0.00005285372,"about_ca_system_score_gemma":0.00007605122,"threshold_uncertainty_score":0.517555},"labels":[],"label_agreement":null},{"id":"W4414015012","doi":"10.1148/ryai.09052025.podcast","title":"BRATS Africa: Building Inclusive AI in Radiology","year":2025,"lang":"en","type":"dataset","venue":"Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Radiology; Medical physics; Medicine; Computer science; Geography; Data science","score_opus":0.1093871306609012,"score_gpt":0.4464637501799323,"score_spread":0.3370766195190311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015012","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010538286,0.01608397,0.013669484,0.04305058,0.026524745,0.0068098237,0.8807352,0.00045395695,0.002133936],"genre_scores_gemma":[0.04533089,0.0062790522,0.0010848914,0.009181677,0.004341019,0.0006095859,0.9322681,0.00006128868,0.00084350473],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99443614,0.00061754376,0.002096927,0.0013380691,0.000303329,0.0012080055],"domain_scores_gemma":[0.996052,0.0015893521,0.00043040924,0.001192737,0.00043112948,0.00030432158],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0012465254,0.00065964187,0.0015478283,0.0014820835,0.0003149838,0.000044224344,0.00074370013,0.0020278234,0.00090548454],"category_scores_gemma":[0.0033514348,0.00066491996,0.00027620955,0.0014040826,0.0007549888,0.00012967276,0.0002875398,0.0026772367,0.00036286516],"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":[0.00050024624,0.00040107983,0.00014962928,0.00033427167,0.00010451828,0.00036809305,0.00053075847,0.00021103292,0.0003835554,0.0030604156,0.95007885,0.04387755],"study_design_scores_gemma":[0.00003669211,0.00087385456,0.000098853605,0.00081649766,0.00026916355,0.0005909111,0.0009344075,0.0010840647,0.0073003704,0.0403462,0.94682676,0.0008222556],"about_ca_topic_score_codex":0.0021237112,"about_ca_topic_score_gemma":0.0017639633,"teacher_disagreement_score":0.051532872,"about_ca_system_score_codex":0.0009166502,"about_ca_system_score_gemma":0.0020474088,"threshold_uncertainty_score":0.99962366},"labels":[],"label_agreement":null},{"id":"W4414283011","doi":"10.1148/ryai.240299","title":"DLMUSE: Robust Brain Segmentation in Seconds Using Deep Learning","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Brain Tumor Detection and Classification","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Neurological Disorders and Stroke; National Institute on Aging","keywords":"Segmentation; Deep learning; Pattern recognition (psychology); Similarity (geometry); McNemar's test; Neuroimaging; Correlation; Pearson product-moment correlation coefficient","score_opus":0.08931687620258674,"score_gpt":0.3399812649188896,"score_spread":0.2506643887163028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414283011","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":[0.61813354,0.000055309578,0.3768272,0.0013709913,0.0008249017,0.00024063882,0.0000010869511,0.000117055955,0.0024292893],"genre_scores_gemma":[0.9974997,0.00002579707,0.0006230234,0.0012145845,0.00005809791,0.000027401615,0.0000025514714,0.000010035616,0.0005388266],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981479,0.00054447,0.00044102359,0.00048455584,0.000092566224,0.00028950078],"domain_scores_gemma":[0.9990282,0.00060906704,0.00012134758,0.00017189032,0.000030179997,0.00003931892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049200986,0.0001305162,0.00016566452,0.000354191,0.00026160254,0.000048454593,0.00020410099,0.00013877546,0.0003682043],"category_scores_gemma":[0.0014146141,0.00014491024,0.000045843783,0.00092735584,0.00022897767,0.00017098497,0.000036602407,0.0004025969,0.00011965798],"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":[0.000059951974,0.00005552517,0.00089786807,0.0000111814215,0.000003169146,0.000012820088,0.0005578864,0.05555548,0.8044414,0.04942276,0.00003367198,0.08894828],"study_design_scores_gemma":[0.0000525746,0.000050029623,0.0014032908,0.000018537577,0.000005344062,0.000044080756,0.0010881943,0.37404412,0.61326706,0.009368311,0.0005018649,0.00015659574],"about_ca_topic_score_codex":0.000028198418,"about_ca_topic_score_gemma":0.00014094522,"teacher_disagreement_score":0.37936616,"about_ca_system_score_codex":0.00017373032,"about_ca_system_score_gemma":0.000057500565,"threshold_uncertainty_score":0.59092677},"labels":[],"label_agreement":null},{"id":"W4414729769","doi":"10.1148/ryai.250137","title":"Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Syllabus; Curriculum; Key (lock)","score_opus":0.1101940586345838,"score_gpt":0.4442563871623562,"score_spread":0.3340623285277724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414729769","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":[0.16513737,0.007685847,0.6878493,0.13216361,0.0021421968,0.004088856,0.0000959822,0.00022635146,0.000610509],"genre_scores_gemma":[0.9706988,0.0009383326,0.007379197,0.017492553,0.001419313,0.0012582903,0.00016123874,0.000029079665,0.00062323024],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975233,0.00030784297,0.0008687299,0.0006817069,0.000096170384,0.0005222218],"domain_scores_gemma":[0.9948873,0.004000394,0.00016960423,0.0005608142,0.00024835643,0.00013356483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010583517,0.00025836527,0.00048656497,0.00011435291,0.00087125815,0.000047403853,0.00031193893,0.00042206998,0.000099389224],"category_scores_gemma":[0.0012119014,0.00020594803,0.00015152383,0.0003162141,0.0005180727,0.00009356977,0.00007292239,0.0007534012,0.00007869888],"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":[0.0004503851,0.00022699256,0.007135408,0.00009773329,0.00017005598,0.0000025495813,0.004240424,0.00006287379,0.0054044207,0.10155739,0.010472663,0.8701791],"study_design_scores_gemma":[0.00020550856,0.001032472,0.0041175224,0.00032922823,0.00066778326,0.00022622831,0.02366276,0.044654164,0.04561375,0.6394047,0.2392292,0.0008566857],"about_ca_topic_score_codex":0.0016019929,"about_ca_topic_score_gemma":0.0005190924,"teacher_disagreement_score":0.8693224,"about_ca_system_score_codex":0.00016014118,"about_ca_system_score_gemma":0.00029741842,"threshold_uncertainty_score":0.8398317},"labels":[],"label_agreement":null},{"id":"W4416364784","doi":"10.1148/ryai.240502","title":"Random Convolutions for Domain Generalization of Deep Learning–based Medical Image Segmentation Models","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"Deutsche Forschungsgemeinschaft","keywords":"Segmentation; Convolution (computer science); Generalization; Dice; Pattern recognition (psychology); Image segmentation; Scale-space segmentation; Medical imaging","score_opus":0.02848134944934683,"score_gpt":0.32593589604278844,"score_spread":0.2974545465934416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416364784","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":[0.0016622714,0.0001955855,0.99442524,0.0025890847,0.00026456118,0.00056793395,0.0000037923855,0.00011436395,0.0001771486],"genre_scores_gemma":[0.60180104,0.00007055098,0.39715698,0.0004566596,0.00006567398,0.0003468049,0.00004291953,0.000008247209,0.000051097606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985097,0.00021260326,0.00052523485,0.00037200947,0.00014479845,0.00023566607],"domain_scores_gemma":[0.99840456,0.00085038436,0.00017303636,0.00029487585,0.00021712205,0.000060020604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004687149,0.000117763506,0.00022057572,0.00014994314,0.00025737507,0.000019633488,0.0005413072,0.00013121277,0.000031126263],"category_scores_gemma":[0.00030661505,0.000120107856,0.00008426085,0.0005989654,0.00029021886,0.0002159307,0.00006563821,0.00014101742,0.000010137176],"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":[0.000043787022,0.00004565514,0.000017450657,0.000010031319,0.000011341435,5.993612e-7,0.0001065294,0.35262376,0.008352932,0.6126512,0.00008980203,0.026046861],"study_design_scores_gemma":[0.000105044586,0.00004041258,0.000009583379,0.000008397265,0.0000075545167,0.0000023249609,0.00002746034,0.68451506,0.029647427,0.28542972,0.00014013585,0.00006686923],"about_ca_topic_score_codex":0.0000064570336,"about_ca_topic_score_gemma":0.000032981297,"teacher_disagreement_score":0.6001388,"about_ca_system_score_codex":0.00005496634,"about_ca_system_score_gemma":0.00013902488,"threshold_uncertainty_score":0.4897856},"labels":[],"label_agreement":null}]}