The Effect of Comorbidities on Cochlear Implantation Outcomes in Adults Under 60
Bibliographic record
Abstract
Introduction: Prior studies have demonstrated that comorbid conditions can negatively impact cochlear implantation (CI) outcomes in elderly patients, but few have examined how comorbidities affect younger adult CI recipients. This study examines the relationship between comorbidities and CI outcomes in adults under 60 years old. METHODS: We reviewed all CI recipients between 20 and 60 years old from 2015 to 2019 at a tertiary academic medical center. Patient data were collected including comorbidities, demographics, etiology, and length of deafness (LOD). Patients' Charlson Comorbidity Index (CCI) was calculated. The primary outcome was speech perception scores at 1 year on the consonant-nucleus-consonant (CNC) word test. RESULTS: There were 118 patients who underwent CI (20-29 years [15%], 30-39 years [22%], 40-49 years [21%], 50-60 years [42%]), averaging 1.8 comorbidities. Mean LOD was 19.7 years, and most etiologies were unknown (53.4%). 34% had no comorbidities, and the most frequent comorbidities were hypertension (14%), asthma (10%), anxiety (8%), acoustic neuroma (8%), and arthritis (7%). Comorbidity frequency was similar across ages, but cardiovascular comorbidities varied by patient decade (50-60 years: 41% vs. 20-49 years: 12-22%, p = 0.004). Compared to studies on elderly CI outcomes, our cohort had fewer comorbidities with reduced cardiac events and neurological conditions. We did not find differences in 1-year CNC scores or complications based on the number of comorbidities or any single comorbidity. However, there was a difference in individual improvement in CNC word scores by age group (p = 0.024). Patients' CCI did not correlate to post-op scores. CONCLUSION: Subjects showed improved speech understanding post-CI. The number and type of comorbidities were not meaningful predictors of 1-year speech perception scores, suggesting adult CI users under 60 years with comorbidities can expect comparable outcomes to those without comorbidities. .
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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 | 0.000 | 0.000 |
| 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".