{"id":"W4296266806","doi":"10.1177/17407745221118366","title":"A Bayesian adaptive design for clinical trials of rare efficacy outcomes with multiple definitions","year":2022,"lang":"en","type":"article","venue":"Clinical Trials","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Toronto; SickKids Foundation; Hospital for Sick Children; Public Health Ontario; Impact; Institute for Clinical Evaluative Sciences; McGill University","funders":"Bill and Melinda Gates Foundation","keywords":"Flexibility (engineering); Computer science; Outcome (game theory); Bayesian probability; Early stopping; Set (abstract data type); Clinical trial; Risk analysis (engineering); Clinical study design; Rare events; Variety (cybernetics); Machine learning; Data mining; Artificial intelligence; Statistics; Medicine; Mathematics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04661229,0.001285003,0.001312872,0.001469935,0.0004635842,0.001543196,0.00202197,0.00241713,0.007312456],"category_scores_gemma":[0.08842772,0.0008350651,0.001829191,0.001043124,0.001676368,0.001517336,0.001850387,0.002387873,0.0008568296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190082,"about_ca_system_score_gemma":0.002110909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004960142,"about_ca_topic_score_gemma":0.000517211,"domain_scores_codex":[0.9354206,0.05730788,0.001386096,0.002073753,0.003338785,0.0004729288],"domain_scores_gemma":[0.9367837,0.05035751,0.005311308,0.003496081,0.00323426,0.0008170673],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.008434989,0.0009083342,0.009583904,0.002298071,0.001186957,0.0003545919,0.0008098495,0.3286414,0.006356671,0.3126442,0.00511485,0.3236663],"study_design_scores_gemma":[0.005465748,0.006442292,0.003713351,0.0009694587,0.0007474158,0.0004869798,0.00008165194,0.7338371,0.003811366,0.2276453,0.01655715,0.0002421859],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009307767,0.0003515756,0.9869126,0.0003743806,0.00006309363,0.001375521,0.0001010003,0.0001969219,0.001317254],"genre_scores_gemma":[0.1970811,0.0004957268,0.7917691,0.000457647,0.00007239907,0.008781495,0.0002209146,0.00005517789,0.001066455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9533877,"threshold_uncertainty_score":0.2465122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9518411116323391,"score_gpt":0.6939768661821792,"score_spread":0.2578642454501598,"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."}}