Factors Affecting Performance in Adults With Cochlear Implants: A Role for Cognition and Residual Cochlear Function
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of preoperative and perioperative factors on postlinguistic adult cochlear implant (CI) performance and design a multivariate prediction model. STUDY DESIGN: Prospective cohort study. SETTING: Tertiary referral center. PATIENTS AND INTERVENTIONS: Two hundred thirty-nine postlinguistic adult CI recipients. MAIN OUTCOME MEASURES: Speech-perception testing (consonant-nucleus-consonant [CNC], AzBio in noise +10-dB signal-to-noise ratio) at 3, 6, and 12 months postoperatively; electrocochleography-total response (ECochG-TR) at the round window before electrode insertion. RESULTS: ECochG-TR strongly correlated with CNC word score at 6 months ( r = 0.71, p < 0.0001). A multivariable linear regression model including age, duration of hearing loss, angular insertion depth, and ECochG-TR did not perform significantly better than ECochG-TR alone in explaining the variability in CNC. AzBio in noise at 6 months had moderate linear correlations with Montreal Cognitive Assessment (MoCA; r = 0.38, p < 0.0001) and ECochG-TR ( r = 0.42, p < 0.0001). ECochG-TR and MoCA and their interaction explained 45.1% of the variability in AzBio in noise scores. CONCLUSIONS: This study uses the most comprehensive data set to date to validate ECochG-TR as a measure of cochlear health as it relates to suitability for CI stimulation, and it further underlies the importance of the cochlear neural substrate as the main driver in speech perception performance. Performance in noise is more complex and requires both good residual cochlear function (ECochG-TR) and cognition (MoCA). Other demographic, audiologic, and surgical variables are poorly correlated with CI performance suggesting that these are poor surrogates for the integrity of the auditory substrate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".