Cochlear Implantation in Cochlear Otosclerosis
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The objective was to correlate implant performance in cochlear otosclerosis to 1) matched control samples, 2) severity of otic capsule involvement, 3) prior ipsilateral surgery, and 4) programming issues. STUDY DESIGN: Retrospective case controlled study. METHODS: Study cohort comprised 30 individuals. Diagnosis was based on prior ear surgery (stapedectomy [n = 18] or fenestration [n = 2]) and/or pathognomonic radiological findings. High-resolution computed tomography images of the temporal bones were assessed by two radiologists and graded (range, 0-3) for the extent of otosclerosis. Operative records were reviewed. Performance, programming visits, and the number of electrode deactivations at 6 months and at 1 year after implantation were determined for the individuals with otosclerosis and compared with a group of matched control subjects. A within-group comparison correlating severity of otosclerosis to the above was carried out. RESULTS: Implant performance in individuals with cochlear otosclerosis was not significantly different from those without. Previous surgery on the side of implantation did not alter performance. Programming difficulty as reflected in the number of visits and electrode deactivation for sound quality reasons were comparable. Deactivation for facial nerve stimulation occurred exclusively in otosclerotics with the most severe radiological disease (grade 3) and was only with non-modiolar hugging electrodes (n = 5). There was no observed difference between the radiological extent of otosclerosis and implant performance. CONCLUSION: Individuals with severe otosclerosis considering cochlear implantation can be counseled to expect similar benefit to those without, regardless of whether prior surgery occurred on the side of implantation or of severity of otic capsule involvement. There is a significant risk of facial nerve stimulation in otosclerotics with grade 3 disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".