Wildfire-related PM2.5 and cause-specific cancer mortality
Bibliographic record
Abstract
Wildfires have devastating effects on society and public health. However, little evidence from population-based cohort has been performed to analyze the relationship of wildfire-related PM 2.5 , an important component of wildfire smoke, with cancer-specific mortality. We aimed to explore this relationship and identify vulnerable populations in UK with lower levels of wildfire-related PM 2.5 exposure. The study consisted of 492394 participants (age: 38–73 years) recruited by UK Biobank during 2004–2010. The cumulative wildfire-related PM 2.5 within 10 kilometers of residence over three years was used as exposure, which was assessed by chemical transport and machine learning models. A time-varying Cox regression was utilized to explore the relationship of exposure with diverse cancer-specific mortality outcomes. Subgroup analyses of a range of potential modifiers were performed. Each 10 μg/m 3 increment of 3-year cumulative exposure was related to a 0.4 % greater risk of total cancer (95 %CI: 1.001–1.007), a 1.1 % greater risk of lung cancer (95 %CI: 1.004–1.018), and a 2.7 % greater risk of lip, oral cavity and pharynx (LOP) cancer (95 %CI: 1.005–1.049). Higher vulnerability in the wildfire-related PM 2.5 -lung cancer relationship was found among participants being retired than those with other employment status. Even lower levels of exposure to PM 2.5 from wildfires were related to elevated mortality risks for cancer from total, lung, LOP, highlighting the importance of wildfire prevention and control. Further investigations are warranted to enrich and extend existing knowledge in this field. • A cohort study assessed wildfire PM 2.5 impacts on cancer-specific mortality in UK. • Low wildfire PM 2.5 was related to increased total, lung, and oral cancer risks. • Retired group was more vulnerable to lung cancer risk related to wildfire PM 2.5 .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".