SS64-02 EVALUATION OF THE EFFECTS OF PESTICIDES AND NOISE ON THE AUDITORY AND VESTIBULAR SYSTEMS OF FARM WORKERS AND ENDEMIC DISEASE COMBAT AGENTS
Bibliographic record
Abstract
Abstract Introduction Pesticides are chemical substances that are widely used in agriculture and public health efforts to control and repel pests in the environment. Among pesticides, insecticides used to combat insects and larvae belong to the organophosphate, chlorinated and pyrethroid chemical groups and are the ones that pose the greatest risk to hearing and balance. The current study aimed to assess the effect of the concomitant exposure to pesticides and noise on auditory and vestibular systems of farm workers and of endemic disease combat agents. Materials and Methods It is a cross-sectional study carried out between 2010 and 2018 in Brazil. The participants underwent hearing threshold measurements, electro-acoustic procedures, electrophysiological tests, and behavioral tests to assess auditory processing skills. Vestibular assessment was only carried out in the experimental group. Results The findings showed differences between groups which were evidenced in the high-frequency audiometry, acoustic reflex testing, tympanometry, evoked otoacoustic emissions and suppression of the emissions, brainstem evoked response audiometry, dichotic digits test, and random gap detection test, with poorer results among the experimental group. The vestibular assessment revealed peripheral vestibular dysfunctions. Conclusions Concomitant exposure to pesticides and noise impaired the auditory and vestibular systems of farm workers and endemic disease combat 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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".