Prognostic Factors Affecting Hearing in Otitis Media With ANCA-Associated Vasculitis Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: To conduct a systematic review and meta-analysis of clinical studies describing the possible prognostic factors affecting hearing outcomes in Otitis media with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (OMAAV) patients. To provide guidance for clinical work, avoiding profound irreversible hearing loss affecting patients’ lives. Methods: A literature search was performed in PubMed, MEDLINE, EMBASE, Cochrane, Scopus, and Web of Science to identify English articles published before December 1, 2022. After screening the articles, the Newcastle–Ottawa Scale (NOS) was used to assess the risk of bias of the extracted literature, and studies with high quality (score > 6) were included. Results: Four studies were included: 1 was a retrospective cohort study, and 3 were case–control studies. We performed a meta-analysis of 4 factors: facial palsy, hypertrophic pachymeningitis, ANCA-negative status, and the period from onset to diagnosis. The results showed that there was a significant association between facial palsy [odds ratio (OR) 1.51; 95% confidence interval (CI) 1.07-2.15; I 2 = 0%; P = .02], hypertrophic pachymeningitis (OR 1.73; 95% CI 1.18-2.53; I 2 = 24%; P = .005), ANCA negativity (OR 1.75; 95% CI 1.11-2.77; I 2 = 33; P = .02), and poor hearing prognosis in OMAAV patients. However, the period from onset to diagnosis (SEM ± SD 2.54; 95% CI −1.56 to 6.64; I 2 = 98%; P = .22) of OMAAV was not significantly associated with poor hearing outcomes. Conclusion: We found that OMAAV patients with facial palsy, hypertrophic pachymeningitis, and ANCA negativity have a significant association with poor hearing prognosis, which provides diagnosis and treatment guidance in protecting patients’ hearing.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".