Cochlear implant failures and reimplantation: A 30‐year analysis and literature review
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The objectives of the study were to present an institutional experience with device failures and cochlear reimplantation rates over a 30-year period and to perform a detailed literature review. STUDY DESIGN: Retrospective institutional experience and literature review. METHODS: A review of cochlear implant failures over a period of 30 years, between January 1988 and March 2017, at a single institution was conducted. Cochlear implant failures were calculated based on manufacturer, type of failure, and overall failure rate. Survival analysis was performed using Kaplan-Meier curves. An electronic search of the PubMed, Web of Science, and EMBASE databases revealed 24 articles on the topic of cochlear device failure. Data on reimplantation and device failure rates were extracted from this literature review and analyzed. RESULTS: A total of 804 cochlear implantations were reviewed from three manufacturers. The institutional reimplantation rate was 2.9% compared to the pooled rate of 6.0% calculated from the literature review. Medical failures accounted for 0.5% of the overall failures, device failures accounted for 1.6%, and inconclusive failures account for 0.7%. Survival analysis revealed a significant difference among manufacturers. An improved device failure rate was noted in the adult population (0.8%) as compared to the pediatric population (2.8%). CONCLUSIONS: This 30-year review represents one of the longest series in the literature examining reimplantation, device failure, and medical failure rates. Cochlear implant survival varied by manufacturer and was significantly better in adult compared to pediatric patients. LEVEL OF EVIDENCE: NA Laryngoscope, 130:782-789, 2020.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.031 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".