Ototoxicity of trichloroethylene in concentrations relevant for the working environment
Bibliographic record
Abstract
Organic solvents can cause hearing loss themselves or promote noise-induced hearing loss. The objective of this study was to review the literature on the effects of low-level exposure to trichloroethylene on the auditory system and consider its relevance for the occupational settings. Both human and animal investigations were evaluated only for realistic exposure concentrations based on the Quebec permissible exposure limits: 50 ppm 8-h time-weighed average exposure value (TWAEV) and 200 ppm short-term exposure value (STEV). In humans, the upper limit for considering ototoxicity data relevant to the occupational exposure situation was set at the STEV. Animal data were evaluated only for exposure concentrations up to 100 times the TWAEV. There is no convincing evidence of trichloroethylene-induced hearing losses in workers. In rats, trichloroethylene affects the auditory function mainly in the cochlear mid- to high-frequency range with a lowest observed adverse effect level (LOAEL) of 2000 ppm. No studies on ototoxic interaction after combined exposure to noise and trichloroethylene were identified in humans. In rats, supra-additive interaction was reported. Further studies with sufficient data on the trichloroethylene exposure of workers are necessary to make a definitive conclusion. In the interim, we recommend considering trichloroethylene as an ototoxic agent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".