Perspective on neuromuscular factors in poststroke fatigue
Bibliographic record
Abstract
Purpose: To summarize the potential origins of fatigue poststroke from a neuromuscular perspective, including stroke-induced alterations at the cortical, spinal and muscle levels. Method: Perspective based on narrative literature review. Results: Fatigue is a highly prevalent, but poorly understood symptom poststroke. Neuromuscular fatigue has central and peripheral origins. Individuals with stroke experienced greater central fatigue and less peripheral fatigue during voluntary contractions of the paretic leg in comparison to healthy participants. Neuromuscular adaptations to stroke create an increased susceptibility to central fatigue, which may be a contributing factor to the increased perception of tiredness during performance of activities of daily living. Future studies should investigate whether intervention-induced cortical plasticity, gains in muscle strength and endurance will attenuate self-reported fatigability. Conclusions: Fatigue is a common and debilitating consequence of stroke. Neuromuscular fatigue of central origin may contribute to self-reported fatigue. Continued focused and properly designed research studies should provide substantial insight into the therapeutic interventions that will improve the management of fatigue poststroke.Implications for RehabilitationFatigue is a common and debilitating consequence of stroke, which has received little attention in clinical rehabilitation.Insufficient understanding of the pathophysiology of poststroke fatigue limits advances in its treatment.Neuromuscular fatigue of central origin may contribute to the self-reported fatigue poststroke.Although speculative, rehabilitation interventions that foster neuroplasticity, muscle strength and endurance may have a role in the management of fatigue poststroke.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".