Incidence of Reportable Exertional Heat Illness during Deepwater Horizon Cleanup
Bibliographic record
Abstract
The purpose of this research was to explore the relationship between incidence of recordable exertional heat illness (EHI) and daily high wet bulb globe temperature (WBGTmax) or daily high Heat Index (HImax). Additionally, the effect of the previous day’s exposure to heat stress was investigated. Illness and injury records were collected during the Deepwater Horizon oil spill response. All OSHA recordable EHI cases were extracted for study. The highest estimated WBGT and HI on the day of incident were compared to the prior day. The overall incidence of recordable EHIs was 0.74 cases/100 FTE and the incidence for exposures ≥ 20 °C WBGTmax was 0.80 cases/100 FTE and 0.87 for exposures ≥ 80 °F HImax. This was much higher than observed in outdoor construction work during the third quarter (summer) in Washington State at 0.16 cases/100 FTE and urban letter carriers in the summer at 0.18 cases/100 FTE. A Poisson regression was used to model the data. The rate ratios were 1.31/°C for WBGTmax and 1.08/°F for HImax. When there was an increase of 2 °C in WBGTmax from the previous day, the predicted incidence was 2.4 times higher than changes < 2 °C. For an increase of at least 10 °F in HImax, the incidence was 2.9 times higher than changes < 10 °F. In conclusion, EHI incidence was related to an increase in WBGTmax or HImax, and if there was an increase in heat stress from the previous day that the incidence was higher. The implication for heat stress management is to increase vigilance as heat stress increases and when there is an increase in heat stress from the previous day.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".