How is neuromuscular fatigability affected by perceived fatigue and disability in people with multiple sclerosis?
Bibliographic record
Abstract
Whereas fatigue is recognized to be the main complaint of patients with multiple sclerosis (PwMS), its etiology, and particularly the role of resistance to fatigability and its interplay with disability level, remains unclear. The purposes of this review were to (i) clarify the relationship between fatigue/disability and neuromuscular performance in PwMS and (ii) review the corticospinal and muscular mechanisms of voluntary muscle contraction that are altered by multiple sclerosis, and how they may be influenced by disability level or fatigue. Neuromuscular function at rest and during exercise are more susceptible to impairement, due to deficits in voluntary activation, when the disability is greater. Fatigue level is related to resistance to fatigability but not to neuromuscular function at rest. Neurophysiological parameters related to signal transmission such as central motor conduction time, motor evoked potentials amplitude and latency are affected by disability and fatigue levels but their relative role in the impaired production of torque remain unclear. Nonetheless, cortical reorganization represents the most likely explanation for the heightened fatigability during exercise for highly fatigued and/or disabled PwMS. Further research is needed to decipher how the fatigue and disability could influence fatigability for an ecological task, especially at the corticospinal level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".