{"id":"W3186606527","doi":"10.1093/biostatistics/kxab028","title":"Bayesian adaptive model selection design for optimal biological dose finding in phase I/II clinical trials","year":2021,"lang":"en","type":"article","venue":"Biostatistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Cancer Institute","keywords":"Bayesian probability; Selection (genetic algorithm); Computer science; Model selection; Maximum tolerated dose; Adaptive design; Econometrics; Clinical trial; Statistics; Artificial intelligence; Mathematics; Medicine; Internal medicine","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":[0.018426,0.001263061,0.001778562,0.0008540148,0.0003691493,0.0009430157,0.001675189,0.001652727,0.002849336],"category_scores_gemma":[0.02254558,0.0008366868,0.0009997208,0.0007716973,0.001460761,0.0009443631,0.001448227,0.002262179,0.0005412047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128533,"about_ca_system_score_gemma":0.002211713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006396098,"about_ca_topic_score_gemma":0.0006056923,"domain_scores_codex":[0.983241,0.01382713,0.0004445083,0.001065143,0.001065192,0.0003570413],"domain_scores_gemma":[0.9872114,0.009234871,0.001522733,0.0007256363,0.0008713147,0.0004340038],"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":[0.003029896,0.0005025092,0.002596361,0.0004199879,0.0002693856,0.0001588039,0.0002306151,0.7630584,0.009684965,0.06831036,0.001699137,0.1500398],"study_design_scores_gemma":[0.000901147,0.001577917,0.0009603419,0.00004797457,0.00009634191,0.00006688918,0.00002161087,0.9612928,0.00293236,0.02963231,0.00241711,0.00005324551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009187951,0.0001572492,0.9891522,0.0002013675,0.00002296138,0.0004552195,0.00004510461,0.0001308353,0.0006470983],"genre_scores_gemma":[0.4128195,0.0002445916,0.5810785,0.0005016621,0.00006383407,0.00346611,0.0001714772,0.00006337767,0.001590851],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.018426,"threshold_uncertainty_score":0.09744716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8908728524028277,"score_gpt":0.6664937251343948,"score_spread":0.2243791272684329,"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."}}