Fatigue Impact Scale Demonstrates Greater Fatigue in Younger Stroke Survivors
Bibliographic record
Abstract
BACKGROUND: Fatigue affects 33-77% of stroke survivors. There is no consensus concerning risk factors for fatigue post-stroke, perhaps reflecting the multifaceted nature of fatigue. We characterized post-stroke fatigue using the Fatigue Impact Scale (FIS), a validated questionnaire capturing physical, cognitive, and psychosocial aspects of fatigue. METHODS: The Stroke Outcomes Study (SOS) prospectively enrolled ischemic stroke patients from 2001-2002. Measures collected included basic demographics, pre-morbid function (Oxford Handicap Scale, OHS), stroke severity (Stroke Severity Scale, SSS), stroke subtype (Oxfordshire Community Stroke Project Classification, OCSP), and discharge function (OHS; Barthel Index, BI). An interview was performed at 12 months evaluating function (BI; Modified Rankin Score, mRS), quality of life (Reintegration into Normal living Scale, RNL), depression (Geriatric Depression Scale, GDS), and fatigue (FIS). RESULTS: We enrolled 522 ischemic stroke patients and 228 (57.6%) survivors completed one-year follow-up. In total, 36.8% endorsed fatigue (59.5% rated one of worst post-stroke symptoms). Linear regression demonstrated younger age was associated with increased fatigue frequency (β=-0.20;p=0.01), duration (β=-0.22;p<0.01), and disability (β=-0.24;p<0.01). Younger patients were more likely to describe fatigue as one of the worst symptoms post-stroke (β=-0.24;p=0.001). Younger patients experienced greater impact on cognitive (β=-0.27;p<0.05) and psychosocial (β=-0.27;p<0.05) function due to fatigue. Fatigue was correlated with depressive symptoms and diminished quality of life. Fatigue occurred without depression as 49.0% of respondents with fatigue as one of their worst symptoms did not have an elevated GDS. CONCLUSIONS: Age was the only consistent predictor of fatigue severity at one year. Younger participants experienced increased cognitive and psychosocial fatigue.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".