Factors Affecting Performance on an Army Urban Operation Casualty Evacuation for Male and Female Soldiers
Bibliographic record
Abstract
INTRODUCTION: This study was conducted to determine what physical and physiological characteristics contribute to the performance of an urban operation casualty evacuation (UO) and its predictive test, FORCE combat (FC) and describe the metabolic demand of the UO in female soldiers. METHODS: Seventeen military members (9 M and 8 F) completed a loaded walking maximal aerobic test, the UO and FC. Heart rate reserve (HRR) and completion time were used as efficiency/performance measures. Oxygen consumption (VO2) was directly measured for UO on five female participants with a portable indirect calorimetry system, and analyzed using descriptive statistics. Stepwise multiple regression analysis was used to determine the contribution of the non-modifiable (age, sex, height) and modifiable characteristics (lean body mass to dead mass ratio (LBM:DM), VO2max corrected for load (L.VO2max), peak force (PF) measured on an isometric mid-thigh pull (IMTP) and medicine ball chest throw distance (Dist) on to the performance of each exercise. RESULTS: LBM:DM and PF were the only factors included in the stepwise regression model for UO, predicting 70% of UO performance (p < 0.01). For FC, L.VO2max only was included in the stepwise regression model predicting 54% of FC performance (p < 0.01). Sex, age and height were not included in the regression model. The average metabolic cost of UO was 21.4 mL of O2*kg-1*min-1 in female soldiers while wearing PPE. CONCLUSION: This study showed that modifiable factors such as body composition, PF on IMTP and L.VO2max are key contributors to performance on UO and FC performance.
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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) | 0.003 | 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".