Association of vitiligo with high‐frequency sensorineural hearing loss: a systematic review and meta‐analysis
Bibliographic record
Abstract
The association between vitiligo and hearing loss has been noted but the specific frequencies and degrees of hearing impairment remain unclear. The objective of this systematic review was to investigate the relationship between vitiligo and hearing thresholds at various specific frequencies. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched MEDLINE and Embase for relevant studies from inception to 10th April 2021. Case-control studies, cross-sectional, or cohort studies that compared the frequency-specific hearing thresholds between vitiligo patients and age-matched non-vitiligo controls were included. There were neither language nor geographic limitations. We used the Newcastle-Ottawa Scale to assess the risk of bias of included studies. The DerSimonian and Laird random-effects model was utilized in meta-analyses due to expected clinical heterogeneity. We included 9 case-control studies with 371 vitiligo patients and 349 controls, which were rated with low or unclear risk. We found neither relevant cross-sectional nor cohort studies. The meta-analysis showed that when compared with controls, vitiligo patients had significantly higher pure-tone hearing thresholds at 2000, 4000, and 8000 Hz. In conclusions, vitiligo patients are prone to high-frequency sensorineural hearing loss.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".