Atmospheric PM <sub>2.5</sub> exposure and risk of ischemic heart disease: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Fine particulate matter <2.5 μm in diameter (PM 2.5 ) has been validated to associate with cardiovascular diseases (CVD) incidence and mortality. So far, no study has quantitatively evaluated the relationship between the atmospheric PM 2.5 exposure and ischemic heart disease (IHD). We conducted a meta-analysis to illustrate the relationship between PM 2.5 and IHD. Published articles were systematically searched (until June 2022) from PubMed, EMBASE, Cochrane Library. A random-effect model was performed to summarize the total relative risks (RRs) and 95% confidence intervals (CIs). Meta-analysis was performed using Stata 12.0 software. A total of 28 studies among 23 cohorts (23.38 million individuals and 256256 IHD cases) were included. With PM 2.5 increasing 10 μg/m 3 , the total RRs of IHD incidence and mortality were 1.07 (95% CI: 0.99–1.17), 1.21 (95% CI: 1.15–1.28), respectively. In sub-analyses, our study revealed that the combined RRs of exposure to PM 2.5 on IHD mortality in Asian and European population [1.11 (95% CI: 0.93–1.33); 1.06 (95% CI: 1.02–1.11)] were much lower compared with American and Canadian people [1.27 (95% CI: 1.17–1.37); 1.30 (95% CI: 1.24–1.35)]. Furthermore, study duration, size and some adjustments were related with the total RR. Our findings indicated that exposure of an increase in the concentration of atmospheric PM 2.5 may increase the risk of IHD incidence and mortality. Further evidence is needed to confirmed the association.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".