Effects of Noise Associated with Pesticides in the Hearing and Vestibular Systems of Endemic Disease Combat Agents
Bibliographic record
Abstract
The current study aimed to assess the effect of the concomitant exposure to noise and pesticides on the auditory and vestibular systems of endemic disease combat agents. The sample comprised 58 participants, males, divided into two groups. The exposed group (EG) comprised 40 agents, adults, exposed to the noise and pesticides. The control group (CG) comprised 18 participants, without exposure, paired according to age range and gender. The participants from both groups underwent conventional pure-tone audiometry and high-frequency audiometry, evoked otoacoustic emissions and suppression of the emissions, immittance testing, brainstem evoked response audiometry, and dichotic digits test. The vestibular assessment was only carried out in the experimental group. Results showed no difference between the groups in the findings of the pure-tone audiometry and suppression effect of the evoked otoacoustic emissions. Difference was evidenced between the groups in the acoustic reflex testing, the tympanometry, the brainstem evoked response audiometry, and the dichotic digits test, with worse results among the EG. In the vestibular assessment, there was the prevalence of altered tests among EG in 36.4% of the cases, more evidence for the peripheral vestibular dysfunction. In conclusion, noise and pesticide exposure impaired the auditory and vestibular systems of endemic disease control agents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".