{"id":"W2517267428","doi":"10.1503/cmaj.160267","title":"Improving precision medicine using individual patient data from trials","year":2016,"lang":"en","type":"review","venue":"Canadian Medical Association Journal","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Precision medicine; Alternative medicine; Personalized medicine; Computer science; Clinical trial; Medicine; MEDLINE; Data science; Family medicine; Medical physics; Bioinformatics; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.09707631,0.002271799,0.009234731,0.009296199,0.0005763845,0.006536775,0.004541339,0.004863289,0.008773102],"category_scores_gemma":[0.3107309,0.00152156,0.00533088,0.01055774,0.002123382,0.007837633,0.003375016,0.006275631,0.002768239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002731967,"about_ca_system_score_gemma":0.00795377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00258976,"about_ca_topic_score_gemma":0.003719137,"domain_scores_codex":[0.9093755,0.05765646,0.009854936,0.005671935,0.0167656,0.0006754833],"domain_scores_gemma":[0.4976625,0.439231,0.02430001,0.02164293,0.01593839,0.001225044],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005036011,0.00008422763,0.00426942,0.06623688,0.009321284,0.000104896,0.0003910348,0.002267746,0.0003429048,0.01818377,0.08199882,0.8162954],"study_design_scores_gemma":[0.001014554,0.0005567918,0.01847778,0.1779704,0.01665756,0.001077214,0.0003941913,0.003403035,0.001312798,0.171274,0.6074582,0.000403471],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008135941,0.9414809,0.02002029,0.02310269,0.002231411,0.0003513128,0.005984607,0.000285898,0.005729252],"genre_scores_gemma":[0.03748885,0.8601807,0.05195406,0.02846536,0.01172713,0.001184505,0.00758959,0.0002422682,0.001167602],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9029237,"threshold_uncertainty_score":0.5133947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6660005388889144,"score_gpt":0.5070505510966773,"score_spread":0.1589499877922371,"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."}}