Respiratory health outcomes of children and adolescents exposed to wildfire smoke: a systematic review
Bibliographic record
Abstract
Wildfire events are increasing in frequency and intensity globally, partly due to climate change. This emerging public health crisis will disproportionately impact vulnerable populations such as children. Epidemiological studies link wildfire smoke, especially fine particulate matter (PM2.5 and PM10), with adverse respiratory outcomes; yet few focus specifically on pediatric populations. This systematic review examines wildfire smoke impacts respiratory health in youth populations by analyzing studies identified through a comprehensive literature search in PubMed and Web of Science through 30 September 2024. Of 120 publications identified, five met the inclusion criteria: three retrospective cohorts, one cross-sectional, and one case-crossover study. Studies were conducted in the U.S. and Canada, using various exposure assessment methods including stationary monitors, satellite imagery, and surveys. Not all studies reported compatible effect measures, vote counting based on the direction of effect, and statistical significance was applied. All studies reported increases in respiratory symptoms, hospital visits, and medication use on days with significant wildfire smoke exposure. Differences in exposure measurement methods, health outcome definitions, and age stratifications limited cross-study comparability. Despite limitations, the review found consistent evidence linking wildfire smoke exposure to worsened respiratory health in children. Further research using standardized exposure assessments and age-specific analyses is needed.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".