Metal Exposures, Noise Exposures, and Audiometry from E-Waste Workers in Agbogbloshie, Ghana
Bibliographic record
Abstract
Metals, such as lead, may be ototoxic, but this property is not well understood, especially in conjunction with noise. This cross-sectional study investigated hearing, noise, and metal biomarkers in informal electronic waste (e-waste) recycling workers in Accra, Ghana. Workers (N = 58) participated in audiometric testing, a survey, blood collection, and personal noise dosimetry. Sixty percent of participants displayed audiometric notches indicative of noise-induced hearing loss (NIHL). Most workers (86%) reported high noise while working. Daily average noise levels were in the range 74.4-90.0 dBA. Linear regression models indicated participants who lived at Agbogbloshie Market for longer periods were significantly associated with worse hearing thresholds at 4 and 6 kHz. The models did not identify blood levels of lead, mercury, or cadmium as significant predictors of worse hearing thresholds or larger noise notches, but increased levels of selenium were significantly associated with better hearing at 6 kHz. Models of thresholds at 4 and 6 kHz were improved by including an interaction term between the maximum noise exposure and the level of zinc in whole blood, suggesting that zinc may protect hearing at lower noise levels, but not at higher levels. Further study of the relationships between elements, noise, and NIHL is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".