Stability of Audiometric Measures and Challenges in Long-Term Management of the Elderly Cochlear Implant Patient
Bibliographic record
Abstract
OBJECTIVE: To determine the long-term audiometric stability and the types and frequency of management challenges encountered when working with elderly cochlear implant users. STUDY DESIGN: Retrospective chart review. SETTING: University hospital. PATIENTS: The final sample is 23 cochlear implantees over the age of 60, followed an average of 8.4 years. INTERVENTION: Rehabilitative (cochlear implantation for clinical purposes, audiologic management). MAIN CLINICAL OUTCOME MEASURES: Warble tone thresholds, spondee thresholds, speech recognition scores, and frequency counts of management problems. RESULTS: Warble tone thresholds were stable over the study period. Speech recognition performance was not significantly changed statistically over the study period, but examination of individual results showed that 26% improved in performance, 17% participants decreased, and 17% remained the same. Thirteen percent of the sample had noteworthy thinning of the flap, including one individual whose flap deteriorated and required explantation.Management challenges relating to failing health, broken and lost equipment, thinning of the skin flap, critical judgment and emotional difficulties during programming sessions, and the need for repeated instructions on device use were noted with varying frequencies. CONCLUSION: Cochlear implantation is beneficial for elderly patients with severe-profound sensorineural hearing loss as demonstrated by long-term stability of function, with the caveat that some individuals may experience significant decreases in speech recognition over time. However, unique management challenges resulting from age-related cognitive decline, health problems, and/or reduced dexterity may present themselves. Audiologists must keep these issues in mind during preoperative counseling and when structuring postoperative follow-up sessions.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".