{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001443317,0.00008770444,0.0001739597,0.0001144907,0.00003010231,0.000009070604,0.00002542773,0.0001207371,0.00005017221],"category_scores_gemma":[0.0002493708,0.00007911088,0.0001885925,0.00007843947,0.00002437823,0.00006675356,0.00002373718,0.00006049559,0.0000483536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001865911,"about_ca_system_score_gemma":0.0000308181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008011434,"about_ca_topic_score_gemma":0.00001655751,"domain_scores_codex":[0.9992819,0.00004328847,0.0001920345,0.0001889704,0.0001352796,0.0001584874],"domain_scores_gemma":[0.9995383,0.00009704813,0.00006556573,0.0001342038,0.00007666132,0.00008819564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.02110078,0.000002129264,0.9618841,0.00002766179,0.0001353835,0.000008347615,0.00009020348,0.0006974053,0.001552713,0.0002044449,0.000710441,0.01358637],"study_design_scores_gemma":[0.008523791,0.004392114,0.4478135,0.000002503869,0.0004129315,0.00003500559,0.000008365199,0.004903872,0.0008225774,0.0005894958,0.5323426,0.0001532383],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901164,0.0001084953,0.004289136,0.002610421,0.001679949,0.0006565715,0.000006422577,0.0001077146,0.0004249564],"genre_scores_gemma":[0.9940318,0.000009847027,0.0002385309,0.0003691804,0.004712979,0.000004148574,0.00002055612,0.00001730973,0.000595659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5316322,"threshold_uncertainty_score":0.3226048,"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."}}