Baseline Function and Rehabilitation Are as Important as Stroke Severity as Long-term Predictors of Cognitive Performance Post-stroke
Bibliographic record
Abstract
OBJECTIVE: Although individuals with low stroke severity tend to recover well, cognitive impairment is common independent of stroke size or location. In this study, the patterns of recovery for individual cognitive domains and factors associated with outcome were examined. DESIGN: A prospectively enrolled cohort of patients with minor stroke was administered cognitive testing at 1, 6, and 12 mos postinfarct. Composite T scores were generated for global cognition and well as independent cognitive domains at each time point. Paired t tests compared changes in scores over time. Regression models identified factors associated with improvement. RESULTS: A total of 46 patients, with an average NIH Stroke Scale score of 2.7, were enrolled. Average age was 61.3 yrs. Patients improved overall between 1 and 6 mos; however, distinct patterns of recovery were seen for different cognitive domains. The most significant improvement was in spatial memory. Verbal memory scores remained low longitudinally. Motor speed and executive function increased, then plateaued. Despite a mean education of 13.6 yrs, only 36% of global cognition scores were higher than or equal to the normative mean at 12 mos, and only 57% of patients improved their global scores from 6 to 12 mos. Late recovery was associated with lower NIH Stroke Scale scores, higher 1-mo Montreal Cognitive Assessment scores, and rehabilitation. Baseline function predicted overall long-term recovery. CONCLUSION: Patterns of recovery are distinct for individual cognitive domains for patients with minor stroke. Stroke severity and rehabilitation influence trajectory. Premorbid baseline predicts long-term outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".