Primary metabolic acidosis induced by four days of simulated combat training: impact of strenuous physical exercise, sleep deprivation, and food restriction
Bibliographic record
Abstract
The military population relies on being mentally and physically healthy to perform well during operations. Recent studies indicate that multiple stressors during combat training influence both acute and chronic stress responses in soldiers, ultimately affecting their performance and health. This study investigated the physiological effects of strenuous physical activity, mental strain, sleep deprivation, and energy deficits during four days of simulated combat training (SCT) by analyzing changes in body composition and blood biomarkers, focusing on metabolic changes. This cohort study with a pre- and post-design included 48 cadets (12 females and 36 males) from the Royal Norwegian Air Force Academy, aged 20-29 years, who participated in mandatory SCT. Body composition was measured using bioelectrical impedance (InBody 770), and blood biomarkers were collected through blood samples and capillary blood gas. We observed a significant decrease in total body weight following SCT, including reductions in total body water, muscle mass, and fat mass. Metabolic markers such as pH, pCO2, and base excess were significantly decreased, while the anion gap significantly increased. Lactate levels showed no significant change following SCT. All electrolyte and nutritional markers (triglyceride, glucose, sodium, calcium, and chloride) significantly decreased, except for potassium, which showed no change. These findings support the necessity for comprehensive monitoring and management of metabolic acidosis, electrolyte imbalances, and hydration status in soldiers undergoing SCT. Ensuring sufficient nutrition, hydration, and recovery time is crucial to reduce negative health effects and maintain optimal performance during and following SCT.
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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 | 0.000 |
| 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) | 0.001 | 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".