The MoxFo initiative—Mechanisms of action: Biomarkers in multiple sclerosis exercise studies
Bibliographic record
Abstract
BACKGROUND: As exercise exerts neurobiological and immunomodulatory effects, it might also act as a disease-modifying intervention in MS. However, a clear mechanistic link between exercise and disease-modifying effects in MS has yet to be established. OBJECTIVE: Establish recommendations for future mechanistic exercise studies in MS. METHODS: In regular meetings, members of the mechanisms of action group within the MoXFo (Moving eXercise research Forward in MS) initiative evaluated gaps of knowledge and discussed unmet needs in mechanistic MS research. RESULTS: We concluded that biomarkers assessed in translational studies in humans and animals are essential to decipher the underlying mechanisms of exercise in MS. Consequently, we defined clear definitions of different types of biomarkers examined in MS exercise studies and operationalized their use to align with the research question and optimal testing time points. Furthermore, we provide key considerations to improve the rigor of translational studies and defined minimal reporting criteria for animal studies. CONCLUSION: The resulting recommendations are intended to improve the quality of future mechanistic exercise studies in MS and consequently lead to a better understanding of therapeutic approaches.
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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 | 0.198 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".