{"id":"W3196511958","doi":"10.1177/09622802211037070","title":"A unified Bayesian framework for exact inference of area under the receiver operating characteristic curve","year":2021,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; Simon Fraser University","funders":"National Cancer Institute; National Heart, Lung, and Blood Institute; Cancer Prevention and Research Institute of Texas","keywords":"Receiver operating characteristic; Inference; Frequentist inference; Computer science; Bayesian probability; Gibbs sampling; Computation; Mathematics; Bayesian inference; Algorithm; Statistics; Mathematical optimization; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0238459,0.0002551914,0.0008579921,0.0001526064,0.0002503024,0.0001043824,0.0007655855,0.0004173756,0.007038642],"category_scores_gemma":[0.5972697,0.0001732147,0.00009374137,0.001128392,0.001393494,0.00005079939,0.0004278704,0.002378031,0.000007626508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001142326,"about_ca_system_score_gemma":0.00116986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007778921,"about_ca_topic_score_gemma":0.0000425974,"domain_scores_codex":[0.9852623,0.009872924,0.001249119,0.0006957033,0.001912563,0.001007395],"domain_scores_gemma":[0.6579202,0.3394111,0.0001537633,0.0008101603,0.001178599,0.0005261921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006614699,0.0001978401,0.0001738298,0.0003007961,0.00003104034,0.00006311695,0.0002822956,9.429383e-7,0.000481093,0.7898254,0.0002487314,0.2083288],"study_design_scores_gemma":[0.000419491,0.0002279799,0.005118956,0.0007496235,0.00002536302,0.00001006137,0.0009839492,0.03197926,0.001308236,0.9586506,0.0003330018,0.0001934473],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001670658,0.0000908435,0.9923384,0.002547462,0.0002495421,0.0006051072,0.0001972166,0.00002179576,0.002278982],"genre_scores_gemma":[0.07990664,0.0001068472,0.9191376,0.0002994515,0.000126595,0.0002441528,0.00001621042,0.00003884912,0.0001236382],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5734237,"threshold_uncertainty_score":0.9999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3778272087755035,"score_gpt":0.6123346236579378,"score_spread":0.2345074148824343,"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."}}