Hearing Loss due to Noise Exposure and its Relationship with Hypertension in Peruvian Workers
Bibliographic record
Abstract
Introduction: Noise-induced hearing loss has been implicated in the genesis of several chronic conditions; however, its behavior concerning hypertension still raises doubts about it. Objective: to determine the association between hearing loss due to exposure to noise and the presence of hypertension in a sample of Peruvian workers. Methods: Cross-sectional analytical study. Secondary analysis of the occupational database of a Medical center in Lima, Peru. hypertension was measured by self-report and clinical method. Hearing loss was classified as none, mild, moderate and severe. For the regression analysis, Poisson was performed with robust variance, obtaining crude (PRc) and adjusted (PRa) prevalence ratios. Results: We worked with a total of 1987 participants. The prevalence of hypertension was 15.40% and hearing loss was 36.39%. For the multivariate regression analysis, a statistically significant association with hypertension was found in those with mild hearing loss (PRa=1.52; CI95% 1.06–2.10), moderate (PRa=2.70; CI95% 1,93–3.76) and severe (PRa=3.82; 95% CI 2.56–5.96), compared to those without hearing loss. Conclusions: Hearing loss due to exposure to occupational noise was associated with the presence of hypertension. Although this study is only a first overview of the relationship that both variables could have, it is recommended to continue promoting policies and awareness campaigns to prevent hearing loss in workers, and thus avoid complications related to it in the long term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".