Muscle fatigue and exercise-related biomarkers in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder affecting motor neurons. The complex etiopathogenetic mechanism of ALS can lead to extensive alterations, including cortical changes, neuroinflammation, and changes in muscular structure. These ALS-derived alterations may contribute to fatigue, a symptom severely impacting patients’ quality of life that is commonly associated with muscular exercise. Intriguingly, muscular exercise can be at once a promoter of motor neuron degeneration in predisposed patients as well as an effective non-pharmacological treatment of ALS. To fully disclose its therapeutic potential, muscular exercise must be tailored to patients’ phenotypes, balancing potential benefits and risks that are unique to each ALS case. Biomarkers of muscular fatigue, with their potential for insight into inflammation and oxidation, can be used to ensure that the intensity of physical activity remains below the threshold level beyond which exercise might become harmful. In this review, the authors explore the concept of fatigue in ALS patients, focusing on fatigue generation, definition, detection, quantification, and treatment. The study discusses the most important fatigue biomarkers, putting them in relation to the mechanism of fatigue generation and with monitoring of muscular exercise as a possible treatment of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".