Associations between short-term exposure to wildfire particulate matter and respiratory outcomes: A systematic review
Bibliographic record
Abstract
BACKGROUND: The frequency and severity of wildfires have been sharply increasing due to climate change, which largely contributes to ambient particulate matter (PM) pollution. We conducted a systematic review focusing on the short-term relationships between PM attributable to wildfires (wildfire-specific PM) and diverse respiratory endpoints, with a comparison between the effects of wildfire-specific PM vs. all-source/non-wildfire PM. METHODS: A comprehensive online search for the literature published from 2000 to 2022 was conducted through PubMed, Web of Sciences, Scopus, and EMBASE. We applied search terms related to wildfire smoke and respiratory health outcomes. RESULTS: ) with respiratory emergency department visits or hospitalizations, with a time-series or case-crossover study design. Studies were mostly conducted in the United States, Canada, and Australia. Positive associations of wildfire-specific PM with respiratory morbidity were observed in most studies. Studies that focused on respiratory mortality were limited. Females can be more vulnerable to the respiratory impacts of wildfire PM, while the evidence of vulnerable subpopulations among different age groups was inconclusive. Few studies compared the effects of wildfire-specific vs. all-source/non-wildfire PM, and some reported higher levels of toxicity of wildfire-specific PM, potentially due to its distinct chemical and physical compositions. Asthma and chronic obstructive pulmonary disease were the most studied diseases, and both were adversely affected by wildfire-specific PM. CONCLUSION: To our knowledge, this is the first review that systematically summarized the associations of wildfire-specific PM exposure with adverse respiratory outcomes and compared associations of wildfire-specific vs. all-source/non-wildfire PM. Further investigations may add to the literature by examining the impacts on respiratory mortality and the effects of specific PM components from different types of wildfires.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".