Occupational Hearing Loss Associated with the Combined Exposure of Solvents and Noise: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
To analyze the predominant frequencies of hearing threshold shift and the prevalence of hearing loss related to the co-exposure to noise and solvents. A systematic review and meta-analysis were performed by retrieving published articles from Web of Science, PubMed, Scopus, Embase, and ProQuest until July 2023. Data were extracted in line with the Cochrane Collaboration Handbook, and the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality were used to assess the studies’ quality. The meta-analysis was used to estimate the odds ratios (ORs) with 95% confidence interval (CI). I2 and Q statistics were used to prove the heterogeneity. A total of 22 selected studies (9948 workers), six cohort studies and 16 cross-sectional studies were included. The results revealed that 43.7%, 41.3%, and 53.6% of the participants presented with hearing loss due to noise exposure, solvent exposure, and combined exposure to noise and solvent, respectively. The workers exposed to both noise and solvents had a higher risk of hearing loss than those exposed to noise (overall weighted odds ratio [OR]: 1.76) or solvents (overall-weighted OR: 2.02) alone. The poorer hearing threshold in the combined noise and solvents exposure group was mainly at high frequencies (3, 4, 6, and 8 kHz), with a peak of 29.47 dB HL at 6 kHz. The noise-exposed group’s peak hearing threshold was 28.87 dB HL at 4 kHz. The peak hearing threshold of the solvent-exposed group was 28.65 dB HL at 6 kHz. The workers exposed to noise and solvent simultaneously had a higher prevalence of hearing loss than those exposed to solvents. Co-exposure to noise and solvents increases the odds of hearing loss. The dominant hearing threshold changes occurred at 3, 4, 6, and 8 kHz, and the peak value appeared at 6 kHz in workers co-exposed to noise and solvents.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.011 | 0.010 |
| 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".