{"id":"W2883218320","doi":"10.1016/j.ijcard.2018.07.128","title":"Although non-stroke outcomes are more common, stroke risk scores can be used for prediction in patients with atrial fibrillation","year":2018,"lang":"en","type":"article","venue":"International Journal of Cardiology","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Population Health Research Institute; University of Calgary; University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Canada Foundation for Innovation; Heart and Stroke Foundation of Canada","keywords":"Medicine; Atrial fibrillation; Stroke (engine); Internal medicine; Cardiology; Stroke risk; Ischemic stroke; Ischemia","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.003989922,0.001128412,0.0007513475,0.001907293,0.0004086751,0.001620729,0.0006218135,0.0005572397,0.002274685],"category_scores_gemma":[0.01273683,0.0002463258,0.001119781,0.001930251,0.000458181,0.0008367211,0.0007153651,0.001053601,0.0005601865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008142573,"about_ca_system_score_gemma":0.00102861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02528403,"about_ca_topic_score_gemma":0.04504548,"domain_scores_codex":[0.9980322,0.0006367962,0.0002826793,0.0002612337,0.0005977171,0.0001893473],"domain_scores_gemma":[0.9919775,0.002729671,0.003165266,0.0005170527,0.001238312,0.0003721487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000112716,0.00004471376,0.9848422,0.00004317207,0.0002751465,0.00002992422,0.00004311036,0.0004674434,0.00006498181,0.000104444,0.0008837851,0.01308845],"study_design_scores_gemma":[0.00002310541,0.0001208541,0.994067,0.00006886165,0.0001591964,0.0001482706,0.0001035359,0.003340833,0.0001528621,0.0006595816,0.001138018,0.00001799177],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971252,0.004437065,0.008174677,0.00156178,0.0001979276,0.0001550925,0.006763297,0.0001481843,0.007309988],"genre_scores_gemma":[0.9937644,0.0008913334,0.002067293,0.0001925239,0.00011299,0.00006047406,0.002209867,0.00001345488,0.0006875203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02528403,"threshold_uncertainty_score":0.05027372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0285311425550153,"score_gpt":0.3297445752696702,"score_spread":0.3012134327146548,"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."}}