{"id":"W4319063464","doi":"10.1002/sim.9677","title":"Conditional concordance‐assisted learning under matched case‐control design for combining biomarkers for population screening","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Roche (Canada)","funders":"University of Texas Health Science Center at Houston; National Center for Advancing Translational Sciences; University of Texas at Austin; National Institute of Diabetes and Digestive and Kidney Diseases; Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston; National Cancer Institute; National Institutes of Health; Cancer Prevention and Research Institute of Texas","keywords":"Concordance; Confounding; Logistic regression; Biomarker; Population; Discriminative model; Prostate cancer; Computer science; Medicine; Case-control study; Oncology; Machine learning; Statistics; Cancer; Internal medicine; Mathematics; Environmental health; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05164962,0.001496683,0.002910389,0.002111865,0.0007611582,0.0016126,0.00373193,0.00204808,0.004020549],"category_scores_gemma":[0.1065256,0.001039306,0.001796377,0.001951811,0.00293836,0.001941412,0.003017493,0.003177228,0.0006561849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190256,"about_ca_system_score_gemma":0.002774193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003220431,"about_ca_topic_score_gemma":0.001801007,"domain_scores_codex":[0.9778863,0.01588402,0.0006820193,0.002684145,0.002317888,0.0005454663],"domain_scores_gemma":[0.9041036,0.07798088,0.004811972,0.006557225,0.005478362,0.00106798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003739445,0.0006988857,0.02150447,0.0007731133,0.001243992,0.0005977245,0.0004165498,0.5796946,0.003114125,0.09831539,0.002858918,0.2870427],"study_design_scores_gemma":[0.0001561918,0.0002854696,0.001149346,0.00002774795,0.0001102504,0.00007577831,0.00001002132,0.9770644,0.0009733237,0.0195553,0.0005602325,0.00003192713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01335774,0.0002336752,0.9850127,0.0001396015,0.00004570069,0.000325258,0.0001419195,0.0003232042,0.0004202607],"genre_scores_gemma":[0.4999889,0.0004624105,0.4923781,0.0005568729,0.0002469627,0.002101572,0.001163127,0.0001237629,0.002978407],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05164962,"threshold_uncertainty_score":0.2731525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.530148671326099,"score_gpt":0.5642905653385226,"score_spread":0.03414189401242362,"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."}}