SS64-01 OTOTOXICITY MANAGEMENT PERSPECTIVES FOR ENVIRONMENTAL AND OCCUPATIONAL EXPOSURES
Bibliographic record
Abstract
Abstract Introduction Ototoxicity management includes the identification of at-risk individuals, and the diagnosis, monitoring, rehabilitation and therapeutic management of hearing and balance deficits in affected individuals. Methods Interested parties committed to addressing major healthcare gaps in the management of ototoxicity (hearing loss, tinnitus, vestibular and/or balance deficits) caused by environmental toxicants and/or medications came together to establish the International Ototoxicity Management Group (IOMG, https://www.ncrar.research.va.gov/ClinicianResources/IOMG.asp). We will report on the objectives and activities of the Environmental and Occupational Focus Area. Results Across countries, no standard methods exist for the auditory surveillance of individuals exposed to hazardous chemicals at work. We published a mixed method review to scope the literature, identify knowledge gaps, appraise results, and synthesize the evidence on the audiological evaluation of workers exposed to solvents. Of 454 identified references, 37 studies were included for analysis. Twenty-five different tests were used in the various studies. Discussion This study is facilitating the development of consensus statements in ototoxicity management in the workplace. Conclusion The IOMG has expanded stakeholder engagement. Its multicultural and interdisciplinary approach is needed to support application of ototoxicity management in specific medical, environmental and occupational contexts worldwide. Disclaimer The conclusions in this presentation are those of the presenter and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".