{"id":"W2967468559","doi":"10.1161/circ.138.suppl_1.17059","title":"Abstract 17059: Re-Use of Clinical Trial Data From the NHLBI Data Repository (BioLINCC) for Patient-Level Meta-Analyses of Cardiovascular Outcomes: Challenges and Opportunities","year":2018,"lang":"en","type":"article","venue":"Circulation","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Compendium; Medicine; Metadata; Clinical trial; Meta-analysis; Data quality; Protocol (science); Systematic review; Psychological intervention; MEDLINE; Alternative medicine; Metric (unit); Internal medicine; Computer science; World Wide Web; Pathology","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":["metaresearch"],"category_scores_codex":[0.6004134,0.002974276,0.006542267,0.02235775,0.002109921,0.01866136,0.0120913,0.003877864,0.01060472],"category_scores_gemma":[0.8204834,0.005144692,0.01035464,0.02905043,0.006468857,0.01270036,0.01364041,0.00953547,0.005273634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006772284,"about_ca_system_score_gemma":0.04289561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00937293,"about_ca_topic_score_gemma":0.01379723,"domain_scores_codex":[0.4536435,0.3599744,0.1045825,0.01635377,0.06423622,0.001209489],"domain_scores_gemma":[0.05229354,0.6753724,0.03743678,0.1680477,0.06461269,0.002236937],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003117851,0.0002438384,0.01787063,0.09451728,0.01765365,0.0005545027,0.006203389,0.00835258,0.005993101,0.05007283,0.393075,0.4023453],"study_design_scores_gemma":[0.004784784,0.0008372061,0.03096524,0.08321154,0.01338303,0.001036329,0.000910772,0.01397458,0.01012624,0.08971141,0.7495499,0.001509052],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01731985,0.04915188,0.5533917,0.100181,0.009306944,0.02409621,0.2052985,0.02111508,0.02013886],"genre_scores_gemma":[0.02798494,0.006839781,0.8648124,0.01251206,0.001676613,0.01868042,0.05860926,0.007152125,0.001732355],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3995866,"threshold_uncertainty_score":0.4927614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9888867629884858,"score_gpt":0.6284819140484117,"score_spread":0.3604048489400741,"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."}}