Abstract NS18: The Impact of Subtle but Significant Executive Function Deficits in Persons with Very Mild Ischemic Stroke
Bibliographic record
Abstract
Background: Executive functions (EF) are essential for independence and engagement in complex activities. Neuropsychological and functional evaluations of stroke outcome may be insensitive to subtle EF deficits present in mild stroke (mCVA) despite low NIHSS scores and excellent outcome based on the Barthel Index (BI>95) and Modified Rankin Scale (MRS< 1). We evaluated EF deficits in 25 individuals with first mCVA (admission NIHSS < 5) and 21 matched controls on the Multiple Errands Test-Revised (MET-R). Methods: Inclusion criteria: age > 18 years, first clinical stroke, functional independence (pre-stroke BI >95 and MRS < 1) and community residence at admission and 6 month follow-up. Demographic and clinical information were obtained at acute admission to acute neurology stroke service. Stroke was confirmed by a neurologist and acute imaging reviewed to score stroke lesions and location and white matter disease. Six month follow-up included a neuropsychological test battery, self-reported outcomes by the Stroke Impact Scale (SIS) and Reintegration to Normal Living Scale (RNLI) and the MET-R, a performance test previously used with TBI. The MET-R is a timed test which requires following standard instructions and rules while performing multiple tasks in a real-world environment. Results: Sample mean age was 60 ± 10.8 years; 65% female and 30 % Black, with no significant demographic differences between groups. The mCVA group reported SIS Global Recovery Scores of 68 ± 21.6, Participation Scores of 73.8 ±24.1 and Memory Scores of 77.2 ± 17.5. Differences between controls and the mCVA group were significant for MET-R sub-scores including: Tasks Completed (p<0.0001), Rules Broken (p<0.001), and Performance Efficiency (p <0.002). Poor performance on the MET-R was associated with lower SIS scores and lower life satisfaction and measured by the RNLI. Conclusion: Persons with Persons with mCVA lacking obvious cognitive or motor impairments may be discharged home without rehabilitation or support because currently available measures are inadequate to identify the presence of EF deficits. Subjective complaints of difficulty with cognitively demand activities at follow-up should be evaluated. Rehabilitation can effectively address these deficits.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".