{"id":"W4316661572","doi":"10.21203/rs.3.rs-2456615/v1","title":"Prevalence and Predictors of Post-stroke Cognitive Impairment among Stroke Survivors in Uganda","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; National Institute of Dental and Craniofacial Research; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; Fogarty International Center; National Institutes of Health","keywords":"Montreal Cognitive Assessment; Medicine; Stroke (engine); Logistic regression; Cognition; Cognitive impairment; Cross-sectional study; Modified Rankin Scale; Physical therapy; Stepwise regression; Internal medicine; Gerontology; Ischemic stroke; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005539785,0.0002722057,0.0003856536,0.001583999,0.0006487435,0.0008724403,0.0003743459,0.0003228305,0.000940304],"category_scores_gemma":[0.002623494,0.0003352309,0.00019063,0.001581811,0.0004171505,0.0005369122,0.0008701368,0.0004339919,0.0001601868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003126254,"about_ca_system_score_gemma":0.0003417637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01082628,"about_ca_topic_score_gemma":0.01138174,"domain_scores_codex":[0.9995684,0.0001136316,0.00006942896,0.00004869978,0.00006966893,0.0001300929],"domain_scores_gemma":[0.9987729,0.0001803851,0.0006997601,0.00004395377,0.0001264897,0.0001765022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002358817,0.0000175467,0.9986697,0.000009352264,0.00001008754,0.00006127229,0.0001900974,0.00001047375,0.00003467111,0.000005396816,0.00003479413,0.0009329647],"study_design_scores_gemma":[0.000002144119,0.00004085713,0.999034,0.00001767015,0.000006876642,0.0002512042,0.0004782196,0.00005194498,0.0000150387,0.00001635374,0.00008359412,0.000002143886],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995177,0.000194521,0.00001115201,0.00002832863,0.000001340793,0.000004315974,0.000088439,0.000001150918,0.0001531076],"genre_scores_gemma":[0.9997024,0.0001554192,0.00002014206,0.00001303,0.00000295829,0.000006147013,0.00005893677,4.516633e-7,0.00004035189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01082628,"threshold_uncertainty_score":0.02152658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03799025438992524,"score_gpt":0.3561656949186999,"score_spread":0.3181754405287747,"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."}}