Stapedotomy vs Cochlear Implantation for Advanced Otosclerosis
Bibliographic record
Abstract
OBJECTIVES: To compare the hearing outcomes of stapedotomy vs cochlear implantation in patients with advanced otosclerosis. DATA SOURCES: PubMed, EMBASE, and The Cochrane Library were searched for the terms otosclerosis, stapedotomy, and cochlear implantation and their synonyms with no language restrictions up to March 10, 2015. METHODS: Studies comparing the hearing outcomes of stapedotomy with cochlear implantation and studies comparing the hearing outcomes of primary cochlear implantation with salvage cochlear implantation after an unsuccessful stapedotomy in patients with advanced otosclerosis were included. Postoperative speech recognition scores were compared using the weighted mean difference and a 95% confidence interval. RESULTS: Only 4 studies met our inclusion criteria. Cochlear implantation leads to significantly better speech recognition scores than stapedotomy (P < .0001). However, this appears to be due to the variability in outcomes after stapedotomy. Cochlear implantation does not lead to superior speech recognition scores compared with the subgroup of successful cases of stapedotomy plus hearing aid (P = .47). There is also no significant difference with respect to speech recognition between primary cochlear implantation and those secondary to a failed stapedotomy (P = .22). CONCLUSIONS: Cochlear implantation leads to a statistically greater and consistent improvement in speech recognition scores. Stapedotomy is not universally effective; however, it yields good results comparable to cochlear implantations in at least half of patients. For cases of unsuccessful stapedotomy, the option of cochlear implantation is still open, and the results obtained through salvage cochlear implantation are as good as those of primary cochlear implantation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".