{"id":"W4384493160","doi":"10.1111/bcp.15841","title":"Bayesian analysis of real‐world data as evidence for drug approval: Remembering Sir Michael Rawlins","year":2023,"lang":"en","type":"editorial","venue":"British Journal of Clinical Pharmacology","topic":"Psychedelics and Drug Studies","field":"Psychology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council Canada; British Pharmacological Society","keywords":"Bayesian probability; Prior probability; Medicine; Computer science; Artificial intelligence","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":[0.08920901,0.0009684487,0.003867621,0.002880466,0.00114514,0.006155984,0.002299832,0.00602966,0.002193293],"category_scores_gemma":[0.3043997,0.00130055,0.002270147,0.002904141,0.01103597,0.01367757,0.002694246,0.02507325,0.000906555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00331173,"about_ca_system_score_gemma":0.003954695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007911504,"about_ca_topic_score_gemma":0.007411793,"domain_scores_codex":[0.9451549,0.04293058,0.002607942,0.002822455,0.006053186,0.0004309848],"domain_scores_gemma":[0.5843596,0.3883852,0.005645843,0.006581946,0.01261142,0.00241596],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008066938,0.0001039102,0.005778736,0.003135551,0.001974347,0.0007116509,0.001452081,0.01252607,0.0002627931,0.1661731,0.5148474,0.2922277],"study_design_scores_gemma":[0.0002192,0.0001134274,0.001838227,0.003752461,0.0002640614,0.000381598,0.0003625825,0.0140309,0.0003099576,0.8019636,0.1764601,0.0003038369],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.001677414,0.1879474,0.04889909,0.7478071,0.01159363,0.00004556523,0.0004544821,0.000132739,0.001442641],"genre_scores_gemma":[0.1428026,0.2265047,0.08599029,0.400301,0.1395853,0.0003652013,0.0005304863,0.0005977332,0.003322766],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.910791,"threshold_uncertainty_score":0.471788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2834852980450441,"score_gpt":0.574456982502008,"score_spread":0.2909716844569639,"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."}}