Status of Hearing Loss and Its Related Factors among Drivers in Zahedan, South-Eastern Iran
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate loss of hearing among drivers in Zahedan, southeastern Iran. PATIENTS & METHODS: This study carried out on a total of 1836 drivers in Zahedan in 2013. Loss of hearing in both ears was measured at 250, 1000, 2000, 3000, 4000, 6000, and 8000 Hertz. The demographic variables, blood parameter and anthropometric data were recorded through interview and examinations. Data were analyzed in Stata.12 software using paired t-tests, McNemar test and Multiple Logistic Regression. RESULTS: The mean age was 38.2±9.8 years. The highest mean hearing thresholds in the right and left ears were 25.7±9.1 and 27.7±9.1, respectively at 250 Hz. There was significant difference between left and right ears hearing threshold at all frequencies (P<0.001), and the highest difference occurred at 250 Hz. Hearing threshold in the left ear was greater than in the right ear at all frequencies. Hearing threshold was correlated to marital status, type of license, and vehicle, smoking, age, and driving history at all frequencies (P<0.01), and also significantly correlated to blood sugar and cholesterol levels at 250 and 500 Hz in both left and right ears (P<0.01). CONCLUSION: In conclusion, high levels of noise increase hearing threshold with greatest damage to the left ear. Therefore, drivers should be periodically examined for ear damage in accordance to variables affecting loss of hearing. Moreover, drivers must be educated about usage of appropriate ear-plugs during driving, especially for the left ear.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".