A weight of evidence approach for the assessment of the ototoxic potential of industrial chemicals
Bibliographic record
Abstract
There is accumulating epidemiological evidence that exposure to some solvents, metals, asphyxiants and other substances in humans is associated with an increased risk of acquiring hearing loss. Furthermore, simultaneous and successive exposure to certain chemicals along with noise can increase the susceptibility to noise-induced hearing loss. There are no regulations that require hearing monitoring of workers who are employed at locations in which occupational exposure to potentially ototoxic chemicals occurs in the absence of noise exposure. This project was undertaken to develop a toxicological database allowing the identification of possible ototoxic substances present in the work environment alone or in combination with noise exposure. Critical toxicological data were compiled for chemical substances included in the Quebec occupational health regulation. The data were evaluated only for noise exposure levels that can be encountered in the workplace and for realistic exposure concentrations up to the short-term exposure limit or ceiling value (CV) or 5 times the 8-h time-weighted average occupational exposure limit (TWA OEL) for human data and up to 100 times the 8-h TWA OEL or CV for animal studies. In total, 224 studies (in 150 articles of which 44 evaluated the combined exposure to noise and a chemical) covering 29 substances were evaluated using a weight of evidence approach. For the majority of cases where potential ototoxicity was previously proposed, there is a paucity of toxicological data in the primary literature. Human and animal studies indicate that lead, styrene, toluene and trichloroethylene are ototoxic and ethyl benzene, n-hexane and p-xylene are possibly ototoxic at concentrations that are relevant to the occupational setting. Carbon monoxide appears to exacerbate noise-induced hearing dysfunction. Toluene interacts with noise to induce more severe hearing losses than the noise alone.
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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 | 0.198 | 0.392 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.107 | 0.036 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".