Effects of body size and load carriage on lower-extremity biomechanical responses in healthy women
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal injuries, such as stress fractures, are the single most important medical impediment to military readiness in the U.S. Army. While multiple studies have established race- and sex-based risks associated with a stress fracture, the role of certain physical characteristics, such as body size, on stress-fracture risk is less conclusive. METHODS: In this study, we investigated the effects of body size and load carriage on lower-extremity joint mechanics, tibial strain, and tibial stress-fracture risk in women. Using individualized musculoskeletal-finite-element-models of 21 women of short, medium, and tall statures (n = 7 in each group), we computed the joint mechanics and tibial strains while running on a treadmill at 3.0 m/s without and with a load of 11.3 or 22.7 kg. We also estimated the stress-fracture risk using a probabilistic model of bone damage, repair, and adaptation. RESULTS: Under all load conditions, the peak plantarflexion moment for tall women was higher than those in short women (p < 0.05). However, regardless of the load condition, we did not observe differences in the strains and the stress-fracture risk between the stature groups. When compared to the no-load condition, a 22.7-kg load increased the peak hip extension and flexion moments for all stature groups (p < 0.05). However, when compared to the no-load condition, the 22.7-kg load increased the strains and the stress-fracture risk in short and medium women (p < 0.05), but not in tall women. CONCLUSION: These results show that women of different statures adjust their gait mechanisms differently when running with external load. This study can educate the development of new strategies to help reduce the risk of musculoskeletal injuries in women while running with external load.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".