Firefighter exposures during structural fires: An overview of real-world data
Bibliographic record
Abstract
This paper examines firefighter exposure to emissions during actual firefighting in building scenarios. The focus is on actual exposure, not small-scale lab tests, to identify research gaps in light of the International Agency for Research on Cancer (IARC) classification of firefighting as an occupation with a heightened risk for certain types of cancer. The work is based on a systematic literature review of all literature published up to 2025, including only studies that measured both emissions and firefighter exposures during actual structure fires. Of the 6860 articles identified from various databases and sources, 76 articles correspond to the inclusion criteria for this review, following a rigorous screening process. Our findings show that, although substantial research has been conducted over the past few decades, the majority of the studies were concentrated in the United States. Fewer studies were identified from Australia, Canada, and select European countries, while no studies identified originated from Africa. Female (women) firefighters were either entirely excluded or represented less than 10 % of study cohorts. These findings underscore critical gaps in both geographic and demographic representation in the existing body of research. Additionally, firefighter exposure during overhaul operations and to ultra fine particles in fire emissions remain largely understudied. • Firefighter exposure has been extensively studied over the past decades. • The literature is geographically thinly spread, mostly concentrated in the US. • Female firefighters are excluded from most studies. • Overhaul phase is relatively understudied. • Firefighter exposure to ultra fine particles is concerning yet understudied.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".