{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009850793,0.0002635901,0.001000358,0.0002798527,0.0002710053,0.0000257717,0.0001455814,0.0001861387,0.0001862333],"category_scores_gemma":[0.1725514,0.0002443414,0.0000779566,0.000461113,0.0002781245,0.00005045255,0.00003026897,0.0003102314,0.000004698751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024304,"about_ca_system_score_gemma":0.00006059226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006917219,"about_ca_topic_score_gemma":0.00004401945,"domain_scores_codex":[0.9961304,0.0009805911,0.001463138,0.0004651255,0.0004384886,0.0005222727],"domain_scores_gemma":[0.8091722,0.1898198,0.0004546766,0.0001771577,0.0002542323,0.0001219603],"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":[0.002160057,0.0001248682,0.002060936,0.00104477,0.0006773513,0.0004670809,0.0003988716,0.006157244,0.0005857436,0.8945986,0.02830857,0.06341589],"study_design_scores_gemma":[0.008610307,0.0005134684,0.00443037,0.0002890968,0.0002708624,0.00003932924,0.000917208,0.2152991,0.00001264005,0.7693537,0.00004828403,0.0002156925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00123582,0.00001980863,0.9937311,0.0006626034,0.0007231455,0.002032814,0.001360863,0.0001809216,0.00005290669],"genre_scores_gemma":[0.1974255,0.000005745872,0.801045,0.0002134271,0.0003096179,0.000445828,0.000364388,0.00006996942,0.000120528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2091418,"threshold_uncertainty_score":0.9963955,"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."}}