Poststroke Fatigue Is Related to Motor and Cognitive Performance: A Secondary Analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Poststroke fatigue (PSF) is a common debilitating and persistent symptom after stroke. The relationship between PSF and motor and cognitive function remains inconclusive partly due to lack of control for effects of depression and use of insensitive measures. We examined the relationship between PSF and motor and cognitive performance using a comprehensive set of behavioral measures and excluding individuals with depression. METHODS: Fifty-three individuals poststroke (16 female) were included (median age: 63 years, median months poststroke: 20 months). Poststroke fatigue was quantified using the Fatigue Severity Scale (FSS) and cognitive performance was measured with the Montreal Cognitive Assessment, simple and choice reaction time (SRT and CRT) tasks. Lower extremity motor performance included Fugl-Meyer Motor Assessment, 5 times sit-to-stand test (5 × STS), Berg Balance Scale, Functional Ambulation Category, and gait speed. Upper extremity motor performance was indexed with Fugl-Meyer, grip strength, and Box and Block test. Spearman correlation and stepwise linear regression analyses were performed to examine relationships. RESULTS: Two motor performance measures, Berg Balance Scale and Functional Ambulation Category, were significantly correlated with FSS (ρ = -0.31 and -0.27, respectively) while all cognitive measures were significantly correlated with FSS (ρ = -0.28 for Montreal Cognitive Assessment, 0.29 for SRT, and 0.29 for CRT). Regression analysis showed that Berg Balance Scale was the only significant determinant for FSS (R = 0.11). DISCUSSION AND CONCLUSIONS: Functional gait, balance, and cognitive performance are associated with PSF. Fatigue should be considered when planning and delivering interventions for individuals with stroke. Future studies are needed to explore the potential efficacy of balance and cognitive training in PSF management.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A287).
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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 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".