{"id":"W7028717291","doi":"","title":"Improving Bayesian adaptive clinical trials with covariate information","year":2024,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Education in Diverse Contexts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","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":"Covariate; Bayesian probability; Prior information; Clinical trial; Bayes' theorem; Bayesian inference","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08723129,0.002102924,0.006016508,0.001623119,0.0007302414,0.00266474,0.003578513,0.004044428,0.008387728],"category_scores_gemma":[0.2837215,0.002751279,0.002855445,0.002084332,0.002027504,0.00390258,0.003863596,0.008176568,0.001626388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085514,"about_ca_system_score_gemma":0.003723603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001730926,"about_ca_topic_score_gemma":0.001494501,"domain_scores_codex":[0.9265457,0.06605402,0.001957833,0.002771029,0.002116053,0.0005553074],"domain_scores_gemma":[0.7645244,0.2174105,0.004896928,0.008206202,0.0033571,0.001604803],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01492301,0.000729341,0.008513587,0.001969968,0.004015609,0.0003501503,0.0005092616,0.3448108,0.001783652,0.04503773,0.02128745,0.5560694],"study_design_scores_gemma":[0.004198636,0.001428715,0.002089357,0.0004029515,0.001571444,0.0002657826,0.00003455821,0.857284,0.001083948,0.1249087,0.006650063,0.00008181055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01652136,0.004882943,0.9689505,0.003676916,0.0003728317,0.00100758,0.0003958457,0.001245692,0.002946411],"genre_scores_gemma":[0.2836598,0.00268245,0.700375,0.002863047,0.0007545626,0.003180169,0.0010832,0.0004774297,0.004924314],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9127687,"threshold_uncertainty_score":0.4613286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08731199515309504,"score_gpt":0.3829980000446256,"score_spread":0.2956860048915305,"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."}}