{"id":"W2215495516","doi":"10.1212/wnl.0000000000002282","title":"Predictors for atrial fibrillation detection after cryptogenic stroke","year":2015,"lang":"en","type":"article","venue":"Neurology","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":170,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cilag; Canadian Institutes of Health Research; Bayer Vital; Biosense Webster; Allergan; National Institutes of Health; H. Lundbeck A/S; Boston Scientific Corporation; European Commission; Sanofi; Daiichi-Sankyo; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; GlaxoSmithKline; Servier; Pfizer; AstraZeneca; Eli Lilly and Company","keywords":"Medicine; Atrial fibrillation; Internal medicine; Cardiology; Hazard ratio; Confidence interval; Stroke (engine); Heart failure; Proportional hazards model; Quartile; Univariate analysis; Multivariate analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007675897,0.000259333,0.0003890093,0.0003601709,0.0002624069,0.0005125954,0.0002010187,0.0004014328,0.001136348],"category_scores_gemma":[0.003955172,0.0001550644,0.0005286142,0.0005282848,0.0002214694,0.0002720291,0.0002636555,0.0006482935,0.000146205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001716486,"about_ca_system_score_gemma":0.000356137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001406985,"about_ca_topic_score_gemma":0.001705638,"domain_scores_codex":[0.9995416,0.0001337383,0.00006191095,0.0001004526,0.0000826313,0.0000796322],"domain_scores_gemma":[0.9969304,0.0007448218,0.001571457,0.0001289812,0.0001968063,0.0004275374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000588187,0.00003512655,0.9976781,0.000006022399,0.00004057991,0.00004184865,0.00001012174,0.00005885042,0.0001224283,0.00000519721,0.000046818,0.001366707],"study_design_scores_gemma":[0.00004618591,0.0004139691,0.9983543,0.000007172598,0.00007048906,0.0003105144,0.00002256877,0.0005286647,0.0001217055,0.0000209568,0.00009934438,0.000004078051],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989999,0.000382617,0.0001219343,0.00006782144,0.000008807179,0.00000811213,0.0001570838,0.000006177194,0.0002476173],"genre_scores_gemma":[0.9996796,0.00006050994,0.00005727191,0.0000153597,0.00001475379,0.000003439714,0.0001225022,8.654656e-7,0.00004568414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001406985,"threshold_uncertainty_score":0.004059434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05224905454552628,"score_gpt":0.3101142938006829,"score_spread":0.2578652392551567,"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."}}