{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03557992,0.0007176586,0.001648566,0.002169727,0.0002731619,0.002078174,0.0008422947,0.001251624,0.002074105],"category_scores_gemma":[0.1578631,0.0003466384,0.001170735,0.001486281,0.000902826,0.001585126,0.0007462662,0.002864632,0.0004679646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001524341,"about_ca_system_score_gemma":0.00209172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002922712,"about_ca_topic_score_gemma":0.002043676,"domain_scores_codex":[0.9878467,0.009389088,0.0009275239,0.0007785279,0.0006892345,0.0003690147],"domain_scores_gemma":[0.6957703,0.2842567,0.008043391,0.005026896,0.005225276,0.00167736],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002368653,0.0009469421,0.1518664,0.00037245,0.0009789572,0.000117152,0.0001252182,0.678838,0.0005129926,0.009360662,0.007799272,0.1467134],"study_design_scores_gemma":[0.0001299877,0.0002650856,0.01340364,0.00006301248,0.0001024151,0.00005173141,0.00001733322,0.9758924,0.0002720941,0.009311763,0.0004754846,0.0000150914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7651505,0.008811406,0.2088539,0.008890446,0.0004047201,0.0003805836,0.002304353,0.0005200306,0.00468396],"genre_scores_gemma":[0.9859586,0.000579559,0.01140885,0.0002843765,0.0001553263,0.00009754051,0.0008598965,0.00003169015,0.0006240816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9644201,"threshold_uncertainty_score":0.1881669,"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."}}