{"id":"W4390705598","doi":"10.1016/j.atherosclerosis.2024.117451","title":"Atherothrombotic and thrombolytic biomarkers in incident stroke and atrial fibrillation-related stroke: The Multi-Ethnic Study of Atherosclerosis (MESA)","year":2024,"lang":"en","type":"article","venue":"Atherosclerosis","topic":"Atrial Fibrillation Management and Outcomes","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre","funders":"National Center for Advancing Translational Sciences; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; National Institutes of Health; Wake Forest University","keywords":"Medicine; Atrial fibrillation; Internal medicine; Stroke (engine); Cardiology; Thrombolysis; Risk factor; Thrombus; Hazard ratio; Proportional hazards model; Biomarker; Myocardial infarction; Confidence interval","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008784762,0.0003558154,0.0006992099,0.0003273457,0.0001482113,0.0001168668,0.0001334794,0.0001810569,0.0001108512],"category_scores_gemma":[0.0001746886,0.0002561007,0.0002659326,0.0007412346,0.0002615102,0.0002320764,0.0001940387,0.0002469843,0.00002268056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001084005,"about_ca_system_score_gemma":0.00005251427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005570471,"about_ca_topic_score_gemma":0.0002917945,"domain_scores_codex":[0.9974698,0.0002285567,0.0008425385,0.0006048894,0.0005031871,0.0003510004],"domain_scores_gemma":[0.9985245,0.0006640491,0.00019652,0.0004563474,0.00004120499,0.0001173509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001265401,0.00003748303,0.9628478,0.0001333238,0.001214596,0.000002408551,0.00618,0.0001950477,0.01863811,0.0001289811,0.00003988423,0.0104558],"study_design_scores_gemma":[0.005050223,0.0006735121,0.9827935,0.0003135298,0.000786984,0.000004477403,0.003595355,0.006068442,0.00020053,0.0000488689,0.0002212681,0.000243301],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926531,0.003406378,0.00004718751,0.001162327,0.0003777664,0.002119245,0.00002028377,0.0001238157,0.00008990308],"genre_scores_gemma":[0.997132,0.001440458,0.0002321307,0.00003366283,0.0002233389,0.000009565966,0.000007200884,0.00007033115,0.0008512958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01994568,"threshold_uncertainty_score":0.9999891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08654453979636036,"score_gpt":0.3447797981745316,"score_spread":0.2582352583781713,"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."}}