Functional rehabilitation criteria required for a safe return to active duty in military personnel following a musculoskeletal injury: a scoping review
Bibliographic record
Abstract
Introduction: The objective of this article is to assess the types of musculoskeletal (MSK) injuries commonly affecting military personnel and the outcome measures that may be used to predict a safe return to active duty post-injury. Methods: A scoping review method was used. The key word-driven electronic search identified 190 articles initially. Thirty-one articles remained following application of inclusion and exclusion criteria. The United States published 27 of the 31 studies, most of which were retrospective reviews, case series, prospective cohort studies, and randomized controlled trials. Results: Based on inclusion frequency, MSK injuries of the shoulder, back, knee, ankle, and foot are the most prevalent in military populations. Physical therapy interventions varied significantly even among similar injury types with return-to-duty rates varying from 2 to 100 per cent over three to 20.9 months, depending on intervention and injury type. Many varied outcome measures were used between studies to evaluate subjects. Discussion: No concrete criteria currently exist to evaluate readiness for a safe return to duty following an MSK injury. More widespread use of standardized protocols for specific injuries and taking into consideration the physical requirements for each military occupational specialty will assist in determining the readiness of recovering soldiers to return to their full duties in the future.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".