{"id":"W4221049038","doi":"10.3889/oamjms.2022.8483","title":"Quantitative EEG Correlates with NIHSS and MoCA for Assessing the Initial Stroke Severity in Acute Ischemic Stroke Patients","year":2022,"lang":"en","type":"article","venue":"Open Access Macedonian Journal of Medical Sciences","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Montreal Cognitive Assessment; Stroke (engine); Confounding; Internal medicine; Acute stroke; Cardiology; Ischemic stroke; Correlation; Electroencephalography; Physical therapy; Cognitive impairment; Ischemia; Disease; 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":[0.0005057115,0.0003338073,0.0002705037,0.0006223022,0.0001345039,0.000368404,0.0001253231,0.0002074653,0.001627314],"category_scores_gemma":[0.003204045,0.000119457,0.0002587191,0.0004316245,0.0001199906,0.0002531158,0.000154778,0.0003303035,0.0002826182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001442028,"about_ca_system_score_gemma":0.0001632463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006644657,"about_ca_topic_score_gemma":0.001037092,"domain_scores_codex":[0.9997376,0.00008110958,0.00004827117,0.00004311478,0.00005951803,0.00003048622],"domain_scores_gemma":[0.9986627,0.0003138757,0.0005844046,0.00007340089,0.000246386,0.0001191926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001925292,0.00007184412,0.9953048,0.00002004384,0.00009557985,0.00006446498,0.00003266522,0.0000868989,0.0005284937,0.00001292284,0.0001341292,0.003455733],"study_design_scores_gemma":[0.000008930872,0.0001651385,0.9990498,0.000006014975,0.0000310813,0.0002180787,0.00003849018,0.0002744966,0.00008885791,0.00003353902,0.00008286694,0.000002627365],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977971,0.0005015719,0.000340544,0.00006638911,0.00001494842,0.00002879688,0.0003313696,0.000009395554,0.0009098718],"genre_scores_gemma":[0.9990824,0.0001576148,0.0002071045,0.00001540652,0.00001985972,0.0000177008,0.0003189833,0.000001852191,0.0001790582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001627314,"threshold_uncertainty_score":0.005443931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09451318049340843,"score_gpt":0.4326522414436507,"score_spread":0.3381390609502423,"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."}}