{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04396248,0.001402315,0.001738646,0.002038117,0.0005357389,0.002717586,0.00170978,0.001780868,0.001970045],"category_scores_gemma":[0.1649715,0.001016462,0.001087751,0.001797481,0.003119678,0.003823712,0.002718322,0.003377039,0.000533977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001870995,"about_ca_system_score_gemma":0.004274595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782692,"about_ca_topic_score_gemma":0.001189405,"domain_scores_codex":[0.9700218,0.0251456,0.0006855886,0.0009521887,0.002857314,0.0003374214],"domain_scores_gemma":[0.9132804,0.07911476,0.002602187,0.002461967,0.002164148,0.0003766282],"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":[0.0002283383,0.000058827,0.001573326,0.000343701,0.000199154,0.0001154268,0.0002306717,0.4064412,0.0004398248,0.4847588,0.001826558,0.1037843],"study_design_scores_gemma":[0.0001007306,0.0001106034,0.0002275754,0.0001070743,0.00004907138,0.00003619294,0.00001529703,0.4670964,0.0004345378,0.5296903,0.002108668,0.00002336262],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001697861,0.000458688,0.9958528,0.0006542644,0.00003486702,0.00009477756,0.00002794122,0.00006909769,0.001109837],"genre_scores_gemma":[0.3757116,0.002995887,0.6160299,0.001124854,0.0005527595,0.001760441,0.0001808997,0.0001204569,0.001523165],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04396248,"threshold_uncertainty_score":0.2324986,"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."}}