A - 78 Characterization of Post-Stroke Cognitive and Mood Impairment within 1-Year Post-Stroke after Hospital Discharge
Bibliographic record
Abstract
Abstract Objective To demonstrate the feasibility of cognitive and psychological characterization after stroke during post-discharge neurology visit as part of standard care. Method From January 1, to April 29, 2023, 33 patients were evaluated using the MoCA and screening tests for aphasia, spatial neglect, depression, and anxiety during their neurology outpatient visit. Neuropsychological measures evaluating attention, processing speed, language, visuospatial, memory, and executive function abilities were also administered. Patients were aged 30–87 years (Mage = 64.8, SDage = 14.2). The sample included 37.1% women and was primarily Black/African American (37.1%) and White (54.3%). The average level of education was some college (Medu = 14.7, SDedu = 32.7). Time between stroke and testing ranged from 0–11 months (Melapsed = 2.8, SDelapsed = 3.1 and 88.6% of patients experienced ischemic stroke. Results Over 68% of patients examined demonstrated global cognitive impairment on the MoCA (MMoCA = 21.2, SDMoCA = 5.1). 5.7% of patients met criteria for spatial neglect and 5.7% met criteria for aphasia. A higher percentage demonstrated impairments within visuospatial or language domains (51.4% visuospatial and 34.3% language, respectively. Further, impairments were observed across all other domains assessed, including attention (22.9%), processing speed (31.4%), verbal memory (62.9%), visual memory (54.3%), and executive function (51.4%). Depression and anxiety were present in 42.9% and 37.1% of the sample, respectively. Elapsed time, type of stroke, lateralization of stroke, sex, or mood scores were not associated with lower performance on the MoCA. Conclusions Cognitive and behavioral deficits following stroke can be identified as part of standard neurologic care that may otherwise have been missed, providing an opportunity to intervene and maximize recovery in stroke patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".