{"id":"W4400969513","doi":"10.1177/09622802241262521","title":"Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling","year":2024,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of Texas at Austin; Pfizer; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; University of Southern California; BioClinica; Bristol-Myers Squibb; Eli Lilly and Company; Biogen","keywords":"Discriminative model; Receiver operating characteristic; Biomarker; Computer science; Artificial intelligence; Biology; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01951289,0.0002216815,0.0004094469,0.0004642461,0.000200907,0.0003519139,0.0009561215,0.0002212771,0.0004782465],"category_scores_gemma":[0.01164732,0.0001902399,0.00004962987,0.001766687,0.0003961135,0.0007285197,0.0008705633,0.002137146,0.00008118749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007483367,"about_ca_system_score_gemma":0.0005702381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003556818,"about_ca_topic_score_gemma":0.00006661537,"domain_scores_codex":[0.9897401,0.005368606,0.0008185493,0.001133707,0.001930019,0.001008986],"domain_scores_gemma":[0.9921864,0.006857831,0.00003697581,0.0004478944,0.0001777662,0.0002931208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005176137,0.00008913627,0.0005387084,0.0002312493,0.00002565556,0.0004879834,0.001425394,0.0005912264,0.001498236,0.006803835,0.00004423451,0.9882126],"study_design_scores_gemma":[0.0002308346,0.0001421144,0.002000514,0.0005822065,0.000004654541,0.00002045579,0.00005985417,0.9843782,0.001583986,0.01064104,0.0001521609,0.0002039333],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03265953,0.0003539761,0.9641966,0.0009438086,0.0005854366,0.0003230583,0.00001402637,0.0000846249,0.0008389349],"genre_scores_gemma":[0.6036077,0.0003029555,0.395708,0.00004530556,0.0001245582,0.000145685,0.000005790317,0.0000227393,0.00003735701],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9880086,"threshold_uncertainty_score":0.996678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1931755343343879,"score_gpt":0.5356551573303712,"score_spread":0.3424796229959832,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}