MSKI reduction strategies: evidence-based interventions to reduce musculoskeletal injuries in military service members
Bibliographic record
Abstract
Musculoskeletal injuries (MSKI) are one of the biggest challenges for military services globally, contributing to substantial financial burdens and lost training and working days. Effective evidence-based intervention strategies are essential to reduce MSKI incidence, and research has shown the positive effect of both nutritional interventions and physical training (PT) interventions on reducing MSKI incidence. Levels of vitamin D metabolites have been associated with MSKI and bone stress fracture risk, while calcium and vitamin D supplementation has been shown to reduce the incidence of stress fractures during military training. Protein and carbohydrate supplementation during arduous military training (high volume, high intensity) has also been shown to reduce MSKI risk and the number of limited/missed duty days. PT has played a key role in soldier development to meet the occupational demands of serving in the armed forces. Paradoxically, while PT is fundamental to enhancing soldier readiness, PT can also be a major contributor to MSKI; emerging evidence suggests that the nature of the PT being performed is a risk factor for MSKI. However, strategies like reducing training load and implementing PT programmes using evidence-based training principles can reduce MSKI incidence among military service members by 33-62%, and reduce the financial burdens for military services. This review provides a summary of effective MSKI reduction interventions and provides strategies to enhance the success and adoption of such interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".