{"id":"W2766569164","doi":"10.1503/cmaj.733412","title":"Federal leadership needed to realize national data set for cardiovascular care","year":2017,"lang":"en","type":"letter","venue":"Canadian Medical Association Journal","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Canadian Cardiovascular Society; University of Toronto","funders":"","keywords":"Set (abstract data type); Health care; Computer science; Data set; Data science; Medicine; Political science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05701387,0.001135424,0.002225661,0.002040767,0.009258032,0.008617108,0.005538355,0.06877669,0.008047354],"category_scores_gemma":[0.2009182,0.001877384,0.003029144,0.001886459,0.008474062,0.01301615,0.008423816,0.09595633,0.006344261],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01292703,"about_ca_system_score_gemma":0.05552883,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04353907,"about_ca_topic_score_gemma":0.07404317,"domain_scores_codex":[0.9477883,0.01371815,0.007540703,0.004412703,0.02228583,0.004254259],"domain_scores_gemma":[0.6999987,0.1783237,0.01098137,0.01310095,0.06306639,0.03452894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002536416,0.00002933978,0.0004360599,0.00004259185,0.0000137906,0.00008931011,0.00007922223,0.00003440514,0.00005430225,0.005881219,0.9875008,0.005813568],"study_design_scores_gemma":[0.00013036,0.00006290641,0.001610088,0.0008827071,0.00004220932,0.000243481,0.000409176,0.0004793571,0.0002599854,0.01169329,0.9840693,0.0001172267],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001068494,0.0003813622,0.0002030138,0.9805058,0.01793658,0.00001443537,0.0001319657,0.0000258886,0.0006943009],"genre_scores_gemma":[0.001387811,0.0003591079,0.001326564,0.9745722,0.02066495,0.00006310091,0.0001522297,0.00003537272,0.001438773],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9870729,"threshold_uncertainty_score":0.3015218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2391558955547689,"score_gpt":0.4404710843264218,"score_spread":0.2013151887716529,"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."}}