{"id":"W2128359271","doi":"10.1007/s00415-012-6508-4","title":"Is pre-existing dementia an independent predictor of outcome after stroke? A propensity score-matched analysis","year":2012,"lang":"en","type":"article","venue":"Journal of Neurology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":89,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Heart and Stroke Foundation; Sunnybrook Health Science Centre; Women's College Hospital; McGill University; Institute for Clinical Evaluative Sciences; University of Toronto; University Health Network; Montreal General Hospital; St. Michael's Hospital","funders":"Canadian Institutes of Health Research","keywords":"Medicine; Dementia; Stroke (engine); Thrombolysis; Intracerebral hemorrhage; Modified Rankin Scale; Neurology; Propensity score matching; Population; Vascular dementia; Emergency medicine; Internal medicine; Physical therapy; Ischemic stroke; Subarachnoid hemorrhage; Disease; Myocardial infarction","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008219722,0.0001830612,0.0008566563,0.0004827889,0.0000261495,0.000009848198,0.0002211412,0.0001286632,0.0003867009],"category_scores_gemma":[0.0001641986,0.0001412694,0.000351738,0.0002624074,0.00008255497,0.0002366929,0.0001949522,0.0005020318,0.000005449238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003757656,"about_ca_system_score_gemma":0.0000413637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002577465,"about_ca_topic_score_gemma":0.00001017312,"domain_scores_codex":[0.9976622,0.0001445376,0.001020189,0.0002024167,0.0005956723,0.0003749104],"domain_scores_gemma":[0.9980134,0.00005285263,0.0009928927,0.0004000154,0.0002621941,0.0002786874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001506795,0.0004126016,0.9874578,0.00007507831,0.002448957,0.0001719084,0.000549681,0.00002589664,0.00665724,0.000003517237,0.0005130701,0.0001774667],"study_design_scores_gemma":[0.001384004,0.001230833,0.9881241,0.00001201526,0.005866113,0.0003254519,0.00003499338,0.0002419231,0.00174776,0.000003476463,0.0009430705,0.00008622922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961742,0.0002176536,0.001667207,0.000896573,0.0003236792,0.0002498557,0.00001277126,0.0000123407,0.0004457776],"genre_scores_gemma":[0.9964454,0.00001113154,0.001216238,0.001772097,0.0003493205,0.000005194981,0.000003308816,0.00002040959,0.0001769222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00490948,"threshold_uncertainty_score":0.5760799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05476497553802177,"score_gpt":0.3106925139636123,"score_spread":0.2559275384255905,"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."}}