Increased Signs of Noise-Induced Hearing Loss in Dental Students
Bibliographic record
Abstract
Context: Despite the fact that the new generations of rotary tools emit less noise, some recent studies suggest that dental students are still at risk of hearing impairment. Aims: The aim of the study was to determine a possible association between noise exposure from dental equipment and early signs of noise-induced hearing loss (NIHL) in dental students. Settings and Design: A cross-sectional study was carried out with dental and non-dental students from two universities in Chile. Methods and Material: A group of 102 dental students routinely exposed to noise emitted from dental equipment was selected as the study group. A group of 251 non-dental students was selected as the control group. Pure-tone audiometry was carried out on all participants ensuring that they were not exposed to noise for at least 24 hours prior to testing. The presence of a notch was determined for each participant. Statistical analysis used: Simultaneous-quantile regressions were used to compare percentiles of the hearing threshold between both groups. Then, the notch prevalence ratio adjusted by gender and age was estimated for each group of participants. Finally, the prevalence of an audiometric notch was compared between both groups using logistic regression models and generalized linear methods. Both fixed effect and multilevel hierarchy models were constructed. Results: Significant differences between groups for the 75th percentile of hearing threshold distributions at 4 and 6 kHz in the left ear and at 6 kHz in the right ear were found. In addition, study group participants exhibited a significantly higher prevalence of a notch at 4 kHz in the left ear than control group participants. Conclusions: Exposure to noise derived from learning activities as a dental student is associated with early signs of NIHL. Such signs include poorer hearing thresholds than those of non-dental students at 4 and 6 kHz as well as the presence of a notch at high frequencies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".