Evaluating High-Functioning Young Stroke Survivors with Cognitive Complaints
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is a commonly used cognitive outcome in stroke trials. However, it may be insufficiently sensitive to detect impairment in high-functioning stroke survivors. The National Institutes of Health (NIH) Toolbox Cognition Battery (NIHTB-CB), a 30-min comprehensive tablet-based cognitive assessment, may be a better choice to characterize cognitive issues in this cohort. METHODS: We compared MoCA and NIHTB-CB performance in young stroke survivors (18-55 years) with excellent functional outcomes (modified Rankin Scale 0-1) reporting subjective cognitive complaints to that of age-matched healthy controls. We recruited 53 stroke survivors and 53 controls. We performed a sensitivity analysis in those participants with normal MoCA scores (≥26). RESULTS: Median MoCA scores were not significantly different between stroke survivors (27.0 vs. 28.0) and healthy controls. Mean T scores for NIHTB-CB fluid (44.9 vs. 54.2), crystallized (53.8 vs. 60.0), and total cognition (49.1 vs. 58.4) components were significantly lower in stroke survivors compared to healthy controls (p < 0.001 for all). In participants scoring within normal range (≥26) on the MoCA, NIHTB-CB scores for all components remained significantly lower in stroke survivors. CONCLUSIONS: In young stroke survivors with excellent functional outcomes and subjective cognitive complaints, the NIHTB-CB, but not the MoCA, was able to detect differences in cognitive performance between stroke survivors and healthy controls. The NIHTB-CB may be a suitable outcome measure for cognition in clinical trials examining higher-functioning young stroke survivors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".