Cochlear Reimplantation Following Device Dysfunction: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To describe the reasons leading to cochlear reimplantation and discuss implications related to technological failures and manufacturer recalls. STUDY DESIGN: Retrospective cohort study over a 40-year period. SETTING: Tertiary academic referral center. PATIENTS: Children and adults who underwent cochlear reimplantation between May 1, 1984, and May 1, 2024. Patients were evaluated, treated, and followed at our center. INTERVENTION: Cochlear reimplantation. MAIN OUTCOME MEASURES: Incidence of cochlear reimplantation, reasons for explantation, and delay between implantation and reimplantation, stratified by age and device manufacturer. RESULTS: A total of 3885 cochlear devices were implanted (66% adults). We report 257 reimplantations, including 86 (33%) pediatric cases at a mean age of 9.6 years. Patients were affected by 4 industry-issued recalls. The overall reimplantation rate was 6.6%, with comparable rates in children (6.5%) and adults (6.7%). Manufacturer-specific reimplantation rates were 12.6% for Advanced Bionics, 3.4% for Cochlear, 8.9% for Oticon, and 1.8% for MED-EL. Recalls and hard failures equally accounted for 70.8% of reimplantations. On average, reimplantations occurred 6.5 years after implantation, and 5.8 years in pediatric cases. Reimplantation delays were significantly reduced for recalls compared with hard failures (-3.7 y; P <0.01) and other causes (-4.7 y; P <0.01). About half (50.4%) of reimplantations occurred within 5 years, and 80.2% within 10 years. CONCLUSIONS: This study highlights the complexity of cochlear reimplantation from both technological and human perspectives in a large population. Given the multifaceted burdens of cochlear reimplantation, future efforts should focus on improving device reliability and care pathways to reduce the need for reimplantation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".