Improving Body Composition May Reduce the Immune and Inflammatory Responses of Firefighters Working in the Heat
Bibliographic record
Abstract
OBJECTIVE: We hypothesized that different body composition and fitness of firefighters would affect immune and inflammatory activity after working in the heat. METHODS: Forty-two firefighters worked in the heat (100 ± 5 °C). Changes in leukocytes, platelets, tumor necrosis factor (TNF-α), and C-reactive protein (CRP) were analyzed based on body composition (DXA) and aerobic fitness (VO2max). RESULTS: Higher baseline leukocytes were observed for high body fat (P = 0.002) and low lean mass (P = 0.023) resulting in the highest peak values. Additionally, significantly lower values for TNF-α were observed with high lean mass at all time points. Platelets were unaffected by fitness or body composition. Furthermore, body mass index (BMI) and VO2max played no role. CONCLUSIONS: Minimizing body fat and increasing lean mass may reduce immune and inflammatory activity of firefighters in the heat.
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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) | 0.002 | 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".