Impedance Change as an Indicator of Cochlear Implant Failure
Bibliographic record
Abstract
OBJECTIVE: In February 2020, Advanced Bionics initiated a field action notice to remove the MS Ultra (V1) implant from circulation. In this study, we quantify a single site's incidence with device failure and examine the relationship between impedance change and declining speech perception. STUDY DESIGN: Retrospective/cohort study. SETTING: Tertiary healthcare center. PATIENTS: Forty-nine adult patients (52 devices) were implanted between October 2017 and December 2019, with the following exclusion criteria: <18 years of age, medical/surgical failures, and lack of English-language proficiency. INTERVENTIONS: Diagnostic. MAIN OUTCOME MEASURES: Postoperative speech perception (AzBio sentence test) scores at 12 months postactivation were compared with repeat testing at 6- to 12-month intervals. Degree of change in impedances from 1 month postactivation was analyzed at similar intervals. Device failures were suspected when impedance levels on three or more electrodes dropped to <3.5 kΩ and AzBio scores declined >15%. Device failures were confirmed through analyses completed by the manufacturer. RESULTS: To date, 21% (11/52) of devices have met all three criteria for a confirmed failure with an average decline in speech perception of 27.7 ± 22%. The average length of time for detection of device failure was 21.9 ± 5 months postimplantation. An additional 7 devices (13%) are currently being monitored for failure but have not yet met full criteria. There was no predictive relationship between degree of impedance change and speech perception scores. Electrodes along the entire array (channels 1-16) can be affected. CONCLUSIONS: Impedance values can be used as a reliable indicator of device malfunction, thus facilitating patient counseling and early intervention. No correlation was identified between impedance changes and speech perception scores.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".