Systematic review of ototoxic pre-surgical antiseptic preparations – what is the evidence?
Bibliographic record
Abstract
OBJECTIVE: There is uncertainty regarding the safety of surgical antiseptic preparations in the ear. A systematic review of the literature was conducted to assess the evidence regarding ototoxicity of surgical antiseptic preparations. METHODS: A literature search was conducted using the PRISMA methods. Key words included "ototoxicity" "hearing loss", "antiseptic", "surgical preparation", "tympanoplasty", "vestibular dysfunction", "chlorhexidine", "iodine", "povidone", "ethanol", and "hydrogen peroxide" using Medline, Embase, Cochrane Library, Scopus and Web of Science. We included peer-reviewed papers that 1) objectively measured ototoxicity in humans or animals through hearing, vestibular function or histologic examination, 2) studied topically applied surgical antiseptic preparations, 3) were either in English or had an English abstract. We excluded papers that were 1) in vitro studies, 2) ear trauma studies, 3) studies of ototoxic ear drops intended for therapy, or 4) case reports. Studies included in the final review were screened using the PRISMA method. Current systematic review registration number pending: 83,675. RESULTS: Fifty-six papers were identified as using PRISMA criteria. After applying our exclusion criteria, 13 papers met overall study criteria. Of these, six papers reported ototoxicity of iodine based solutions, five papers reported ototoxicity of chlorhexidine and ethanol and two papers assessed hydrogen peroxide. All papers reviewed were animal studies. Iodine based solutions show least harm overall, while chlorhexidine and high concentrations of alcohol based solutions showed most harm. The evidence on hydrogen based solutions was inconclusive. CONCLUSIONS: The overall evidence for anyone antiseptic solution is weak. There is some evidence that iodine, chlorhexidine, hydrogen peroxide and alcohol based antiseptics have ototoxicity. Conclusive evidence for human ototoxicity from any solution is not strong.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".