16 weeks of moderate intensity resistance exercise improves strength but is insufficient to alter brain structure in Gulf War Veterans with chronic musculoskeletal pain: a randomized controlled trial
Bibliographic record
Abstract
Introduction: Chronic widespread musculoskeletal pain (CMP) is a primary condition of Veterans who were deployed to the Persian Gulf War. The mechanisms that underlie CMP in these Veterans are unknown and few efficacious treatment options exist. This study tested the effects of 16 weeks of resistance exercise training (RET) on gray matter (GM) volume and white matter (WM) microstructure in Gulf War Veterans (GWVs) with CMP compared to GWV waitlist controls (WLC). Methods: = 26). Training involved 10 resistance exercises to involve the whole body, was supervised and individually tailored, and progressed slowly to avoid symptom exacerbation. Outcomes assessed at baseline, 6, 11 and 17 weeks and 6- and 12-months post-intervention included GM volume (voxel-based morphometry), WM microstructure (diffusion tensor imaging), pain [short form McGill Pain Questionnaire (SF-MPQ) and 0-100 visual analog scale (VAS)], fatigue (0-100 VAS), and mood (Profile of Mood States). Muscular strength was assessed at baseline, 8 and 16 weeks, and training volume was tracked throughout the 16-week intervention. Primary analyses used linear mixed effects models with Group, Time, and the Group*Time interaction as fixed factors and subject and slope as random factors to test the differential effects of RET and WLC on brain structure and symptoms. All neuroimaging analyses used the False Discovery Rate to correct for multiple comparisons at an alpha of 0.05. Results: > 0.05). Conclusion: Sixteen weeks of low-to-moderate intensity RET (i) improved musculoskeletal strength and (ii) did not exacerbate symptoms, but (iii) was insufficient to alter brain structure in GWVs with CMP.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".