{"id":"W4404435360","doi":"10.1093/jrsssc/qlae058","title":"Bayesian optimization for personalized dose-finding trials with combination therapies","year":2024,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada","keywords":"Bayesian probability; Personalized medicine; Computer science; Bayesian optimization; Medicine; Medical physics; Artificial intelligence; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008371309,0.0004409831,0.001579666,0.00006231874,0.0004730535,0.0004666127,0.000487215,0.0002617446,0.00110748],"category_scores_gemma":[0.02925061,0.0002582991,0.0005469132,0.0003123298,0.0008038259,0.0001414351,0.00008466069,0.0007411455,0.000004170749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002832175,"about_ca_system_score_gemma":0.0003582278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003733048,"about_ca_topic_score_gemma":0.000002647073,"domain_scores_codex":[0.9945529,0.001037368,0.002325817,0.000416426,0.001160493,0.0005070168],"domain_scores_gemma":[0.9245002,0.07322281,0.001217255,0.0002917057,0.0005436845,0.000224333],"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":[0.001677831,0.0001938162,0.00001212827,0.0007449502,0.001134985,0.00001350313,0.001008322,0.001568561,0.00008131185,0.9425778,0.03801788,0.01296891],"study_design_scores_gemma":[0.002990341,0.0008191852,0.00008928533,0.0002962176,0.001446838,0.00002443636,0.0008293851,0.06175098,0.0001496876,0.9276798,0.003548247,0.0003756296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001244348,0.0001438726,0.9930589,0.001456005,0.00128809,0.001251816,0.002264911,0.0000773811,0.0003345958],"genre_scores_gemma":[0.01916978,0.00007852798,0.9788772,0.0002082872,0.0006505253,0.0000919221,0.00002125365,0.0001158607,0.0007866598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07218543,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2485891949827273,"score_gpt":0.4745265667534945,"score_spread":0.2259373717707672,"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."}}