Bibliographic record
Abstract
The link between noise and cardiovascular disease was among the earliest to be examined by researchers interested in the non-auditory effects of noise. While the earliest studies (pre-1980) suffered many limitations, they motivated a body of research in the 1980’s and 90’s that produced more consistent and increasingly valid findings relating transportation noise and CVD, and produced a biological model. Since then, a number of higher quality studies have probed the roles of different noise sources, and a greater range of outcomes. There is good evidence that noise is an independent risk factor for disease when co-exposure with air pollutants occurs. Increasingly studies have looked at the “intermediate” and other effects of noise in the cardio-metabolic pathway predicted by the model. Still the amount of research being done in this area is relatively low. A 2018 systematic review by the group updating the Environmental Noise Guidelines for the European Region (van Kempen, Cara, Pershagen and Foraster) reviewed evidence for an association between noise and cardiovascular and metabolic effects. They concluded that the most comprehensive evidence was available for road traffic and ischemic heart disease; a meta-analysis of 7 longitudinal studies gave a relative risk of 1.08 (95% CI 1.01-1.15) per 10 dB(LDEN). The most common outcome studied was hypertension but the authors rated the overall quality of evidence “very low”. Other outcomes that have been examined include stroke, adiposity, obesity and diabetes. For these outcomes there are much fewer studies; those that exist are of variable quality and their findings are often inconsistent. Nevertheless, there is a plausible biological model linking noise with highly prevalent chronic disease outcomes in humans, so there is a great need for more studies and studies of stronger study designs, e.g. longitudinal, with improved exposure assessment and better control of potential confounding and other biases.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".