{"id":"W3092379046","doi":"10.1002/pst.2073","title":"Utilizing Bayesian predictive power in clinical trial design","year":2020,"lang":"en","type":"article","venue":"Pharmaceutical Statistics","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact; University of British Columbia","funders":"","keywords":"Bayesian probability; Interim; Interim analysis; Computer science; Adaptive design; Machine learning; Computation; Clinical trial; Predictive power; Clinical study design; Artificial intelligence; Data mining; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007148369,0.0004263998,0.001362138,0.00006987657,0.0000905986,0.00007944616,0.0005614191,0.000373247,0.002200296],"category_scores_gemma":[0.2659346,0.0003925858,0.0002198656,0.0005107569,0.0005446509,0.00009896429,0.0002668338,0.001687694,0.0001870642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013229,"about_ca_system_score_gemma":0.0002098745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002864145,"about_ca_topic_score_gemma":0.000001129822,"domain_scores_codex":[0.9896669,0.004574076,0.003074999,0.0009644861,0.0008596558,0.0008598648],"domain_scores_gemma":[0.8824727,0.1155036,0.0003823479,0.0003795533,0.000167004,0.00109482],"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":[0.1638341,0.003391318,0.00199515,0.0004114036,0.0004887429,0.001073197,0.0007426134,0.00006846635,0.00008118281,0.6790748,0.0532038,0.09563528],"study_design_scores_gemma":[0.05122584,0.003013958,0.0004709634,0.00006644979,0.0003311906,0.000004289274,0.00008842823,0.09578563,0.0002348478,0.8446875,0.00351141,0.0005794695],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009750669,0.00004536113,0.9909415,0.00142683,0.001352785,0.002055431,0.0005335478,0.000218129,0.002451334],"genre_scores_gemma":[0.1562919,0.00008200984,0.8399673,0.002660813,0.0008012603,0.00008532997,0.000004462324,0.00008477647,0.00002213073],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2587862,"threshold_uncertainty_score":0.9998526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8404547068645039,"score_gpt":0.6558272472679741,"score_spread":0.1846274595965297,"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."}}