Noise exposure and the risk of cancer: a comprehensive systematic review
Bibliographic record
Abstract
The association between noise exposure and increased risk of cancer has received little attention in the field of research. Therefore, the goal of this study was to conduct a systematic review on the relationship between noise exposure and the incidence of cancer in humans. In this study, four electronic bibliographic databases including Scopus, PubMed, Web of Science, and Embase were systematically searched up to 21 April 2022. All types of noise exposure were considered, including environmental noise, occupational noise, and leisure or recreational noise. Furthermore, all types of cancers were studied, regardless of the organs involved. In total, 1836 articles were excluded on the basis of containing exclusion criteria or lacking inclusion criteria, leaving 19 articles retained for this study. Five of nine case-control studies showed a significant relationship between occupational or leisure noise exposure and acoustic neuroma. Moreover, four of five case-control and cohort studies indicated statistically significant relationships between environmental noise exposure and breast cancer. Of other cancer types, two case-control studies highlighted the risk of Hodgkin and non-Hodgkin lymphoma and two cohort studies identified an increased risk of colon cancer associated with environmental noise exposure. No relationship between road traffic and railway noise and the risk of prostate cancer was observed. In total, results showed that noise exposure, particularly prolonged and continuous exposure to loud noise, can lead to the incidence of some cancers. However, confirmation of this requires further epidemiological studies and exploration of the exact biological mechanism and pathway for these effects.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".