Ambient PM2.5 and risk of emergency room visits for myocardial infarction: impact of regional PM2.5 oxidative potential: a case-crossover study
Bibliographic record
Abstract
BACKGROUND: Regional differences in the oxidative potential of fine particulate air pollution (PM2.5) may modify its impact on the risk of myocardial infarction. METHODS: A case-crossover study was conducted in 16 cities in Ontario, Canada to evaluate the impact of regional PM2.5 oxidative potential on the relationship between PM2.5 and emergency room visits for myocardial infarction. Daily air pollution and meteorological data were collected between 2004 and 2011 from provincial monitoring sites and regional estimates of glutathione (OP(GSH)) and ascorbate-related (OP(AA)) oxidative potential were determined using an acellular assay based on a synthetic respiratory tract lining fluid. Exposure variables for the combined oxidant capacity of NO2 and O3 were also examined using their sum (Ox) and a weighted average (Ox (wt)) based on their redox potentials. RESULTS: In total, 30,101 cases of myocardial infarction were included in the analysis. For regions above the 90(th) percentile of OP(GSH) each 5 μg/m(3) increase in same-day PM2.5 was associated with a 7.9 % (95 % CI: 4.1, 12) increased risk of myocardial infarction whereas a 4.1 % (95 % CI: 0.26, 8.0) increase was observed in regions above the 75(th) percentile and no association was observed below the 50(th) percentile (p-interaction = 0.026). A significant 3-way interaction was detected with the strongest associations between PM2.5 and myocardial infarction occurring in areas with high regional OP(GSH) and high Ox (wt) (p-interaction < 0.001). CONCLUSIONS: Regional PM2.5 oxidative potential may modify the impact of PM2.5 on the risk of myocardial infarction. The combined oxidant capacity of NO2 and O3 may magnify this effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".