Meta-analysis on correlations between short-term exposures to 6 common air pollutants and incidence of ischemic stroke
Bibliographic record
Abstract
BackgroundPrevious studies using meta-analysis to explore the relationship between air pollution exposure and ischemic stroke (IS) mostly focus on particulate matter-related themes, few include gaseous pollutants in the study, and subgroup analyses of factors such as different lag days, seasons, and genders are rarely been reported.ObjectiveTo quantitatively evaluate the relationships between short-term exposures to 6 common air pollutants, including fine particulate matter (PM2.5), inhalable particulate matter (PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), and the incidence of IS.MethodsA systematic search was conducted to collect literature studying the 6 common air pollutants and IS published up to May 1, 2022 in 6 databases (China Journal Full-text Database, China Biology Medicine Disc, PubMed, Cochrane Library, Web of Science, and Embase). Literature quality evaluation was performed using the Newcastle-Ottawa Scale. Stata 16.0 software was used to conduct meta-analysis including heterogeneity test, combined effect size, meta-regression, subgroup analysis, sensitivity analysis, and publication bias test.ResultsA total of 33 articles were qualified for inclusion. The total number of samples included in the literature was 7195631. The meta-analysis results showed that short-term exposures to PM2.5 (OR=1.0082, 95%CI: 1.0049−1.0116), PM10 (OR=1.0017, 95%CI: 1.0008−1.0026), CO (OR=1.0328, 95%CI: 1.0231−1.0426), NO2 (OR=1.0150, 95%CI: 1.0079−1.0222), SO2 (OR=1.0158, 95%CI: 1.0078-1.0238), and O3 (OR=1.0017, 95%CI: 1.0003−1.0032) were associated with an increased risk of IS. PM10 and O3 increased the risk of IS in both lag0 and lag1, while PM2.5, CO, NO2, and SO2 all showed an associated increased risk of IS only in lag0. The results of sensitivity analysis showed stable results for all pollutants studied, and there was no publication bias in the literature on the association of the remaining five pollutants with IS incidence except for the PM2.5-related literature.ConclusionShort-term exposures to PM2.5, PM10, CO, NO2, SO2, and O3 may increase the incidence of IS, with this risk showing the most significant level on the day of IS onset.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.073 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".