Associations Between Self-Reported Burn Pit Exposure and Functional Status, 1990-2021
Bibliographic record
Abstract
INTRODUCTION: The Airborne Hazards and Open Burn Pit Registry (AHOBPR) allows service members to self-report exposure to burn pits during military deployments and functional status (a composite metric of physical fitness status). This study investigated whether general exposure to burn pits, specific performance of burn pit duties, or the cumulative number of days deployed in Southwest Asia was associated with a change in functional status. MATERIALS AND METHODS: A retrospective cross-sectional analysis of 234,061 participants in the AHOBPR who completed questionnaires before August 2021 was conducted. Exposure was presumed if an individual reported any burn pits exposure during deployment or if an individual reported having to work at a burn pit as part of their duties and was quantified by the cumulative-reported exposure days. The outcome was the reported composite functional score. Statistical analysis used linear regression, which was adjusted for significant variables. A possible dose-response effect from cumulative deployment and burn pits exposure days was evaluated. Statistical significance was determined at P < 0.05. RESULTS: The burn pit exposure groups were notably different in size (exposed: 230,079, non-exposed: 3982) and were significantly different for all compared variables. There was a negative association between cumulative exposure days and functional score with a significant test for trend. There was a marginal positive significant association between cumulative deployment days and functional score with a significant test for trend. Reporting exposure to burn pit duties was also significantly associated with a lower functional score. CONCLUSION: This study suggests a dose-response relationship between cumulative burn pit exposure and decreased functional status. It also suggests a modest positive relationship between cumulative deployment days and reported function, which may represent a "healthy deployer" effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".