{"id":"W4220947435","doi":"10.1161/strokeaha.121.036557","title":"Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry","year":2022,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Stroke (engine); Machine learning; Predictive modelling; Artificial intelligence; Physical therapy; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01240298,0.0007830074,0.001211249,0.003296747,0.0003600488,0.001507369,0.001223237,0.0006837845,0.0009026096],"category_scores_gemma":[0.02183009,0.0005958325,0.001054139,0.002155058,0.0004444011,0.001423481,0.001261262,0.001016901,0.0005124549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001512107,"about_ca_system_score_gemma":0.001500226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01671583,"about_ca_topic_score_gemma":0.01075637,"domain_scores_codex":[0.9956185,0.002694403,0.0003962546,0.0006138637,0.0004410715,0.0002358455],"domain_scores_gemma":[0.9840876,0.009744497,0.002126853,0.002645275,0.0008926418,0.0005031073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007055173,0.0002974504,0.9174969,0.00005110525,0.000500593,0.0002271758,0.0001404798,0.04721699,0.0001023496,0.0003790795,0.001439947,0.03144257],"study_design_scores_gemma":[0.0002122582,0.0004798257,0.5834019,0.0001194301,0.0005123555,0.000427442,0.0003208874,0.4101537,0.00100265,0.001619099,0.001668707,0.00008172623],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919852,0.0003009847,0.004640908,0.0002473813,0.00001691354,0.00007150235,0.002073595,0.0001292981,0.000534259],"genre_scores_gemma":[0.9898691,0.000276808,0.004625641,0.00002355205,0.00002211714,0.00007225521,0.004803101,0.00001994078,0.0002875631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01671583,"threshold_uncertainty_score":0.06559402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0793463916564323,"score_gpt":0.3333513842698453,"score_spread":0.254004992613413,"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."}}