Risk of sensorineural hearing loss with macrolide antibiotics: A nested case‐control study
Bibliographic record
Abstract
OBJECTIVES: To determine the association between a diagnosis of sensorineural hearing loss (SNHL) and the prescription of a macrolide antibiotic. STUDY DESIGN: Retrospective nested case-control study. METHODS: From the LifeLink (IMS, Danbury, CT) health claims database, we randomly selected a cohort of subjects 15 to 60 years old from 2006 to 2014. Cases were identified as patients diagnosed with SNHL, each matched by age and calendar time to 10 controls selected from the same cohort. All macrolide prescriptions (erythromycin, azithromycin, clarithromycin, and telithromycin) were identified, and statistical comparison of usage was compared between cases and controls. Amoxicillin and fluoroquinolone antibiotics were used as positive controls to further investigate confounding by infection. Albuterol was used as a negative control because this is a drug class not expected to be associated with SNHL or with a confounding condition potentially causing SNHL. RESULTS: From a cohort of 6,110,723 subjects, we identified 5,989 cases of SNHL and 59,890 corresponding controls. The rate ratio for one prescription of a macrolide was 1.36 (95% confidence inteval [CI]: 1.24-1.49) and for multiple prescriptions was 1.66 (95% CI: 1.42-1.94). Similar rate ratios were observed with multiple prescriptions of amoxicillin and fluoroquinolones. CONCLUSION: A significant association between SNHL and macrolide use was likely due to confounding by indication for antibiotic treatment because the risk was also observed with fluoroquinolones and amoxicillin, antibiotics with no known ototoxic potential. Therefore, there does not appear to be an increased risk of SNHL in patients treated with macrolide antibiotics. LEVEL OF EVIDENCE: 3b. Laryngoscope, 127:229-232, 2017.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".