Air pollution and childhood respiratory consultations in primary care: a systematic review
Bibliographic record
Abstract
Background Outdoor air pollution is a known risk factor for respiratory morbidity worldwide. Compared with the adult population, there are fewer studies that analyse the association between short-term exposure to air pollution and respiratory morbidity in children in primary care. Objective To evaluate whether children in a primary care setting exposed to outdoor air pollutants during short-term intervals are at increased risk of respiratory diagnoses. Methods A search in Medline, the Cochrane Library, Web of Science and Embase databases throughout March 2023. Percentage change or risk ratios with corresponding 95% CI for the association between air pollutants and respiratory diseases were retrieved from individual studies. Risk of bias assessment was conducted with the Newcastle–Ottawa Scale (NOS) for cohort or case–control studies and an adjusted NOS for time series studies. Results From 1366 studies, 14 were identified as meeting the inclusion criteria. Most studies had intermediate or high quality. A meta-analysis was not conducted due to heterogeneity in exposure and health outcome. Overall, studies on short-term exposure to air pollutants (carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter ≤10 µm (PM10)) were associated with increased childhood respiratory consultations in primary care. In general, exposure to ozone was associated with a reduction in respiratory consultations. Conclusions The evidence suggests CO, SO2, NO2, PM10and PM2.5are risk factors for respiratory diseases in children in primary care in the short term. However, given the heterogeneity of the studies, interpretation of these findings must be done with caution. PROSPERO registration number CRD42022259279.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".