Audiological outcomes after revision stapes surgeries: a systematic review
Bibliographic record
Abstract
PURPOSE: Revision stapes surgery is a challenging procedure performed in relatively small numbers compared to other middle ear procedures. Despite numerous data on hearing results of different middle ear surgeries, the audiological standards for successful outcome of this procedure are still not clarified. On the basis of well-documented data, we wanted to determine what the expected audiological results and complications are after revision stapes surgery in order to set a realistic threshold for surgical success. METHODS: After the protocol registration in the PROSPERO database, a systematic review was performed in multiple databases (PubMed, Cochrane, Web of Science, Scopus, ScienceOpen, ClinicalTrials.gov, Google Scholar) according to PRISMA guidelines. Twelve articles were reviewed according to the inclusion criteria. A total of 1032 cases were obtained for evaluation. A modified version of Newcastle-Ottawa Scale (NOS) was used to assess publication quality. RESULTS: Average air-bone gap (ABG) gain was 17.3 dB, average air conduction (AC) gain was 17.5 dB. The average postoperative air-bone gap was 11.1 dB. The postoperative ABG distribution was the following 0-10 dB: 53.3%, > 10-20 dB: 28.2%, > 20 dB: 18.5%. SNHL as a surgical complication was described in a total of 17 cases (1.6%), no equilibrium disorder was reported. CONCLUSION: The pooled data suggest that revision stapes surgery is an effective solution after failure of previous stapes surgery. However, the results are clearly inferior to those of primary stapedotomies. Hence, we need to apply different expectations and use different standards in the indication and evaluation of this type of surgery.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".