Environmental stressors and cardio-metabolic disease: part I–epidemiologic evidence supporting a role for noise and air pollution and effects of mitigation strategies
Bibliographic record
Abstract
Traffic noise and air pollution together represent the two most important environmental risk factors in urbanized societies. The first of this two-part review discusses the epidemiologic evidence in support of the existence of an association between these risk factors with cardiovascular and metabolic disease. While independent effects of these risk factors have now clearly been shown, recent studies also suggest that the two exposures may interact with each other and with traditional risk factors such as hypertension and type 2 diabetes. From a societal and policy perspective, the health effects of both air pollution and traffic noise are observed for exposures well below the thresholds currently accepted as being safe. Current gaps in knowledge, effects of intervention and their impact on cardiovascular disease, will be discussed in the last section of this review. Increased awareness of the societal burden posed by these novel risk factors and acknowledgement in traditional risk factor guidelines may intensify the efforts required for effective legislation to reduce air pollution and noise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".