Abstract P210: Cognitive Testing During Mild Acute Ischemic Stroke Predicts Return to Work, Driving, and Good Functional Outcome at 3 Months
Bibliographic record
Abstract
Introduction: Many patients after a mild ischemic stroke (IS) do not return to work or driving. Cognitive testing is commonly done to assess long-term cognitive impairment after IS. However, inpatient cognitive testing during the acute period of mild IS may also be a predictor for workforce reengagement and functional outcome. Methods: At our comprehensive stroke center, we prospectively enrolled previously working adults age <65 years who were diagnosed with first-ever IS, had a prestroke modified Rankin Scale (mRS) ≤1 and NIHSS ≤3. Testing performed within 1 week of IS included the Montreal Cognitive Assessment (MOCA), Clock Drawing Test, Trail Making Tests A and B, Backward Digit Span Test, and Hospital Anxiety and Depression Scale (HADS). Other data obtained included age, gender, years of education, occupation (blue collar or white collar), stroke location (cortical, subcortical, or posterior fossa), stroke laterality, and presence of white matter disease on imaging. Outcome measures assessed at 3 months post stroke included return to work, return to driving, and mRS ≤1. In a logistic regression analysis, we performed both univariate and multivariate analyses. Multivariate analysis was completed on variables p ≤0.05 in the univariate analysis. Results: Of 40 total IS patients enrolled (median [IQR] age 55 [46-60] years; 22.5% female), 36 completed 3-month follow up. 58% returned to work, 78% returned to driving, and 72% had mRS of 0-1 at 3 months. In multivariate analysis, a single point increase in the Clock Drawing Test score increased the odds of return to work by 3.79 (95% CI, 1.10-14.14) and return to driving by 6.74 (95% CI, 1.22-37.23). MOCA and HADS were both associated with mRS ≤1. Conclusion: Clock Drawing Test in the acute period after mild IS may predict early return to work and driving at 3 months. MOCA and HADS may also predict good functional outcome. These tools can potentially be used for prognosis and identifying those who may benefit from further interventions.
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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) | 0.005 | 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".