The Adverse Effects of Heavy Metals with and without Noise Exposure on the Human Peripheral and Central Auditory System: A Literature Review
Bibliographic record
Abstract
Exposure to some chemicals in the workplace can lead to occupational chemical-induced hearing loss. Attention has mainly focused on the adverse auditory effects of solvents. However, other chemicals such as heavy metals have been also identified as ototoxic agents. The aim of this work was to review the current scientific knowledge about the adverse auditory effects of heavy metal exposure with and without co-exposure to noise in humans. PubMed and Medline were accessed to find suitable articles. A total of 49 articles met the inclusion criteria. Results from the review showed that no evidence about the ototoxic effects in humans of manganese is available. Contradictory results have been found for arsenic, lead and mercury as well as for the possible interaction between heavy metals and noise. All studies found in this review have found that exposure to cadmium and mixtures of heavy metals induce auditory dysfunction. Most of the studies investigating the adverse auditory effects of heavy metals in humans have investigated human populations exposed to lead. Some of these studies suggest peripheral and central auditory dysfunction induced by lead exposure. It is concluded that further evidence from human studies about the adverse auditory effects of heavy metal exposure is still required. Despite this issue, audiologists and other hearing health care professionals should be aware of the possible auditory effects of heavy metals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".