{"id":"W3137640584","doi":"10.1161/str.52.suppl_1.p63","title":"Abstract P63: Post-Stroke Cognitive Complaints and Normal MoCA Scores: A Possible Role for Machine Learning-Augmented Cognitive Screening","year":2021,"lang":"en","type":"article","venue":"Stroke","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Montreal Cognitive Assessment; Medicine; Cognition; Stroke (engine); Receiver operating characteristic; Audiology; Physical medicine and rehabilitation; Physical therapy; Cognitive impairment; Internal medicine; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00302163,0.000644428,0.0006257628,0.001042491,0.0002678585,0.001145656,0.0006836469,0.0008162211,0.004230889],"category_scores_gemma":[0.01228293,0.0001496474,0.0004139772,0.0006775684,0.0007088522,0.000803208,0.0004265145,0.0006779814,0.0007184565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003138091,"about_ca_system_score_gemma":0.0004594281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824457,"about_ca_topic_score_gemma":0.001372133,"domain_scores_codex":[0.9993587,0.000251066,0.00006657505,0.0001305367,0.0001283699,0.00006465624],"domain_scores_gemma":[0.9940488,0.003027465,0.001441469,0.0004940458,0.000663711,0.000324534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001997476,0.0002596096,0.9080719,0.0001310345,0.0002391608,0.0008464728,0.0001880619,0.001797271,0.004537405,0.0003785379,0.000841499,0.08071163],"study_design_scores_gemma":[0.0000273137,0.0006693569,0.9839563,0.00005171888,0.00007565926,0.001419861,0.00007965842,0.01002953,0.00168526,0.00152789,0.0004622486,0.00001528638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990649,0.0008922245,0.004147991,0.0007562482,0.00005037351,0.00004284646,0.0003122018,0.0001399296,0.003009006],"genre_scores_gemma":[0.9982988,0.0000724519,0.001128307,0.00004687435,0.00003582056,0.00001184006,0.0001044218,0.000008192753,0.000293265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004230889,"threshold_uncertainty_score":0.01598006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608867642757603,"score_gpt":0.2712181746158586,"score_spread":0.2551294981882826,"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."}}