Relationship between short-term exposure to sulfur dioxide and emergency ambulance dispatches due to cardiovascular disease
Bibliographic record
Abstract
Background: The relationship between sulfur dioxide (SO2) and cardiovascular disease (CVD) remains inconclusive. We aimed to clarify the association between short-term exposure to SO2 and emergency ambulance dispatches (EADs) due to CVD. Methods: We collected daily data on the number of EADs due to CVD, air pollutants, and meteorological factors between October 2013 and June 2018 in Guangzhou, China. We used the quasi-Poisson generalized additive model combined with a distributed lag nonlinear model to estimate the short-term effect of SO2 on EADs due to CVD in multivariable models. Subgroup and sensitivity analyses were also performed. Results: A total of 37,889 EADs due to CVD were documented during the study period. The average daily SO2 concentration was 12.5 μg/m3. A significant relationship between SO2 and EADs due to CVD was found, with a relative risk of 1.04 (95% confidence interval: 1.02, 1.06) with each 10 μg/m3 increment of SO2 at lag 0-1. The relationship was stronger in males, for participants aged ≥65 years, and in the cold season; however, no significant modification by subgroup was found in the association between SO2 and EADs due to CVD. Similar results from sensitivity analyses to the main findings were observed. Conclusions: Short-term exposure to SO2 was significantly associated with increased EADs due to CVD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".