{"id":"W4224271903","doi":"10.1208/s12248-022-00703-3","title":"Machine Learning Prediction of Clinical Trial Operational Efficiency","year":2022,"lang":"en","type":"article","venue":"The AAPS Journal","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Roche (Canada)","funders":"","keywords":"Clinical trial; Duration (music); Resource (disambiguation); Variety (cybernetics); Computer science; Operational efficiency; Operations research; Risk analysis (engineering); Medicine; Artificial intelligence; Engineering; Business; Marketing","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.05753001,0.00008360269,0.0005356072,0.0001580905,0.0009717455,0.00005290863,0.0003722527,0.00004475287,0.00301128],"category_scores_gemma":[0.004582778,0.00007885002,0.0002024455,0.000154226,0.00007071934,0.0001965833,0.00009593248,0.0008324051,0.0001567668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001876084,"about_ca_system_score_gemma":0.0002635256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009753865,"about_ca_topic_score_gemma":0.000003387587,"domain_scores_codex":[0.9931248,0.001574118,0.004707711,0.0002018806,0.0001961403,0.0001953341],"domain_scores_gemma":[0.9956487,0.001030237,0.002953601,0.0002158907,0.00005976805,0.00009177413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0086763,0.002191593,0.3569303,0.0001248016,0.000679806,0.000008920398,0.01142448,0.2034952,0.00002839574,0.2961777,0.1165472,0.003715272],"study_design_scores_gemma":[0.04468199,0.00341943,0.0507438,0.00003435899,0.00003822083,0.0003266054,0.00265921,0.2548215,0.000003733332,0.02494515,0.6179463,0.000379757],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9500068,0.002504054,0.00547855,0.03187662,0.004747488,0.0006012043,0.0003026166,0.00002624985,0.004456383],"genre_scores_gemma":[0.9942533,0.0001670597,0.000411156,0.00227517,0.001689825,0.00003143898,0.00002632054,0.00001566864,0.001130039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.501399,"threshold_uncertainty_score":0.9979001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5523893940679866,"score_gpt":0.4899970778868973,"score_spread":0.06239231618108931,"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."}}