Low Pediatric Cochlear Implant Failure Rate<subtitle>Contributing Factors in Large-Volume Practice</subtitle>
Bibliographic record
Abstract
OBJECTIVE: To evaluate the rate of cochlear implant (CI) failure and CI reimplantation (CIri) in our population of children receiving implants by means of a technique that includes device fixation with suture to cortical bone. DESIGN: Retrospective analysis from January 1990 to June 2010. SETTING: Tertiary pediatric hospital. PATIENTS: A total of 971 devices were provided to 738 children (5575 implant-years). INTERVENTIONS: Cochlear implant explants and CIri. MAIN OUTCOME MEASURES: Surgical findings at CIri were assessed by device model. The Pediatric Ranked Order Speech Perception score and the Phonetically Balanced Kindergarten score were used to make comparisons of hearing ability before and after CIri. RESULTS: Thirty-four patients have undergone CIri in our pediatric center during the past 20 years. Excluding 7 of these patients who received their initial implant at other centers, our rate of CIri was 2.9%. Mean (SD) time to device failure was 61 (43) months. A disproportionately high number of patients (7 of 35 [20%]) requiring CIri had meningitis before implantation. After CIri, children maintained or improved their best speech performance measured before device failure, with only 2 children showing a significant reduction in speech perception after CIri. CONCLUSIONS: A very low rate of failure occurs in children who receive CI devices, and several factors may account for this low rate. Children who develop meningitis before CI appear to be at an increased risk of device failure.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".