{"id":"W2900482092","doi":"10.1177/1740774518815648","title":"Commentary on Hay et al.: Can clinical trials data collection be improved by administrative data elements?","year":2018,"lang":"en","type":"letter","venue":"Clinical Trials","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Data collection; Clinical trial; Hay; Medicine; Statistics; Computer science; Mathematics; Internal medicine; Animal science","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":[0.04566092,0.001815374,0.004619544,0.002262291,0.01026522,0.01117533,0.008925119,0.1348395,0.01377724],"category_scores_gemma":[0.2213721,0.002582459,0.003875875,0.004350283,0.009124753,0.01259817,0.005428403,0.111503,0.01610028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01490115,"about_ca_system_score_gemma":0.02994009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02535467,"about_ca_topic_score_gemma":0.0413051,"domain_scores_codex":[0.9584714,0.01496836,0.005906581,0.00472031,0.01250043,0.003432838],"domain_scores_gemma":[0.841967,0.1071522,0.00811896,0.003690217,0.02656105,0.01251061],"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":[0.00003672563,0.000008404916,0.0001021572,0.00004768081,0.00001170178,0.0001124279,0.00007781017,0.00002075655,0.00001935212,0.001245584,0.9965342,0.001783097],"study_design_scores_gemma":[0.0003466053,0.00005619924,0.000727761,0.00104853,0.00007179134,0.0004123189,0.0004761199,0.0003578739,0.0001814246,0.01241627,0.9837752,0.0001297324],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.00005217413,0.0005052752,0.00004130633,0.9871873,0.01138612,0.00001378277,0.0000988687,0.00001618425,0.0006989618],"genre_scores_gemma":[0.0003535313,0.0001925237,0.00009281214,0.987368,0.01085015,0.0000438672,0.00002375149,0.00001563389,0.001059702],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9543391,"threshold_uncertainty_score":0.2414809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9470349349086055,"score_gpt":0.6933538306100809,"score_spread":0.2536811042985245,"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."}}