Putting your best weighted foot forward: Reviewing lower extremity injuries by sex in weighted military marching
Bibliographic record
Abstract
Introduction: The Nijmegen Marches are an annual event in which military participants complete four consecutive days of 40-kilometre marches. Load carriage requirements differ among nations. The objective of this review is to apply a Gender-based Analysis Plus (GBA+) lens to injury prevention and consider the appropriateness of sex-based load carriage requirements. Methods: Two independent literature searches were undertaken using the MEDLINE database. A title and abstract screening review was conducted of articles reporting on gait biomechanics or injury risk related to load carriage that were published in English in the past 30 years. Thirteen articles were included in the review. Results: Four studies looked at sex-specific biomechanical adaptations to load carriage, with two reporting no significant differences and two reporting differences that may increase the risk of hip or knee injuries among women. Individuals who marched with a stride length longer than their natural or preferred length had more range of motion in their lower extremities and greater impact force; thus, they may be at a higher risk of injury. Weight carried was also identified as a risk factor, but the literature suggests that both sexes respond similarly to increased load as a proportion of body mass. Discussion: On the basis of the literature reviewed, no evidence-based recommendations can be made for sex-specific load carriage requirements in endurance marching. The published literature suggests that event standards for load carriage based on individual characteristics such as body weight or height may be more effective for injury prevention.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".