70 Occupational hazards in dentistry: chemical exposures and compensation claims analysis
Bibliographic record
Abstract
Background The dental workforce includes dentists, dental hygienists, assistants, technicians, and denturists. They work in a rapidly evolving medical field, in which they may face many occupational hazards such as bioaerosols, chemicals, sharp instruments, noise, and stress. Objective The project aim is to document the evidence on chemical exposures in dentistry and to analyze compensation claims from dental workers. Methods First, a structured literature review was conducted using three concepts: Occupational exposure, Dentistry, and Chemicals. Inclusion criteria were publication year between 2000 and 2022, in French or English, and comprising quantitative measurements of chemical exposures in dentistry from a high-income country. Next, compensated claims from dental workers in the Quebec Workers’ Compensation database were analyzed for years 2005 to 2019. Claims were stratified by occupation, sex, age, and type of injury. Annual rates were calculated for injuries potentially associated with chemical exposures. Results Twenty-eight articles were included in the literature review. Chemicals measured were mercury (57% of articles), nitrous oxide (18%), methacrylates (14%), and silica (11%). Exposures to mercury up to 3.3 mg/m³ were measured in a dental school. In the compensation claims database, 2229 claims were filed by dental workers over a 15-year period, 96% of them coming from women. While there were no explicit cases of poisoning, there were 331 needlestick injuries and 70 claims for exposures to caustic substances, including phosphoric acids and peroxides. Furthermore, there were 11 claims for contact dermatitis, 4 for allergic dermatitis, and 6 for respiratory illnesses. The annual claim rates for injuries potentially associated with chemical exposures remained relatively stable over the 15 years, averaging 4.0 claims per 1000 workers (95%CI: 3.0-5.0). Conclusions The variety of chemical hazards highlighted in our study, together with technical developments such as 3D printing, confirm the growing need for updated data on actual exposures in dentistry. (BMJ)
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.041 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".