Global association between short-term exposure to sulfur dioxide and ischemic stroke: evidence from a meta-analysis
Bibliographic record
Abstract
TPS 683: Short-term health effects of air pollutants 2, Exhibition Hall, Ground floor, August 28, 2019, 3:00 PM - 4:30 PM Background/Aim: Air pollution exposure has been linked to hospital admissions for ischemic stroke (IS), yet the individual triggering effect of ambient sulfur dioxide (SO2) on acute IS remains unclear. Our study aimed to investigate the short-term association between SO2 and IS hospitalizations by meta-analytic approach. Methods: We systematically searched Medline, EMBASE and Web of Science to Feb 2019 for time series and case-crossover studies which reported the association of daily increases in SO2 with hospital admissions for IS. We adapted a quality scale from OHAT and New Castle Ottawa to assess risk of bias of eligible studies. Adjusted relative risks (RRs) with corresponding 95% confidence intervals (CIs) for a standardized increment (10 parts per billion [ppb]) in SO2 were combined using random-effect model. Heterogeneity within studies was evaluated with I2 statistic and explored by meta-regression analysis. Results: Of 974 identified citations, 154 articles were reviewed in depth with 17 included in overall meta-estimates. Hospital admissions for IS was not significantly related to a 10 ppb increase in SO2 concentrations (RR: 1.010; 95% CI: 0.996-1.023) with significant heterogeneity detected (I2=60.0%). In subgroup analyses, similar results were found in both time series and case-crossover studies. Further meta-regression showed that the high heterogeneity could be explained by geographical location (P=0.001). The summary estimates for Europe, North America and Asia were 0.996 (0.989-1.004), 1.099 (0.965-1.253) and 1.014 (0.999-1.028), respectively. For single-day lag analyses, a non-significant increase in IS hospitalizations was observed on the same day and previous day of SO2 exposure, but the case was inverse on the lag 2 (RR: 0.997; 95% CI: 0.972-1.022). Conclusion: Our study indicated a non-significant adverse transient association between SO2 and IS hospitalizations. The lack of such association is intriguing and greater caution should be taken when interpreting this finding; while it may prove informative for future research.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".