Approaches to managing ototoxicity in the workplace
Bibliographic record
Abstract
Objective Ototoxic chemicals in the workplace can pose a risk to hearing and balance functions. Our objective was to identify evidence-based practices for occupational health settings in managing ototoxicity. This resulted in the document, Health Management of Workers Exposed to Ototoxic Chemicals, created by the International Ototoxicity Management Group.Design To develop a practical approach for any workplace, we reviewed a variety of sources and used an international panel of interdisciplinary experts. Evidence included data from experimental, observational, and review studies. Thirty-two subject matter experts were invited to review the document; twenty-two completed the review and unanimously endorsed the ototoxicity management system as proposed.Results Six key action steps were proposed to: (1) identify workers exposed to ototoxic chemicals, (2) perform auditory and vestibular assessments, (3) follow-up after monitoring health, (4) document worker data, (5) maintain healthy safety culture, and (6) review ototoxicity management approach. These steps focus on the management of workers who are at-risk for workplace ototoxic chemical exposure at any level (with or without concurrent noise exposures).Conclusions Early identification strategies include self-report questionnaires; auditory testing; vestibular screening; referrals for diagnosis; management of cases; and monitoring of exposure scenarios to prevent further cases.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".