Multiple sclerosis and exercise—A disease-modifying intervention of mice or men?
Bibliographic record
Abstract
Research suggests that physical exercise can promote an anti-inflammatory and neuroprotective state. If so, increasing or optimizing exercise could be considered a 'disease-modifying intervention' in neuroinflammatory diseases, such as multiple sclerosis (MS). Exercise intervention studies conducted in animal models of MS are promising. Various aerobic and strength training regimes have been shown to delay disease onset and to reduce both the clinical and pathological disease severity in mice. However, fundamental differences between the physiology of animals and humans, the disease states studied, and the timing of exercise intervention are significant. In animal models of MS, most exercise interventions begin before disease initiation and before any clinical sign of disease. In contrast, studies in humans recruit participants on average nearly a decade after diagnosis and often once disability is established. If, as is thought to be the case for disease-modifying treatments, the immunomodulatory effect of exercise decreases with advancing disease duration, current studies may therefore fail to detect the true disease-modifying potential. Clinical studies in early disease cohorts are needed to determine the role of exercise as a disease-modifying intervention for people with MS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.005 | 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".