Cochlear Implant Electrode Deactivation Produces Higher Individual but Lower Average Outcome Scores
Bibliographic record
Abstract
OBJECTIVES: To evaluate cochlear implant (CI) performance in recipients with an intact electrode array compared to a subset with one or more electrodes deactivated to examine differences in speech perception outcomes. METHODS: A review of a single CI centre database of adult recipients using an Advanced Bionics HiRes Mid-Scala electrode was performed. Comparisons between recipients with a fully functioning ("intact") array and recipients with one or more electrodes deactivated included aided thresholds and sentence recognition scores (AzBio). RESULTS: Forty-eight of 164 recipients (29%) had one or more electrodes deactivated (mean = 2.5, median = 2.0, SD = 1.4). Reasons included sound quality concerns (n = 22), lack of percept (n = 16), non-auditory stimulation (n = 4), incomplete insertion (n = 3), and abnormal loudness growth (n = 3). There were no significant differences in pre-operative status or demographics between the cohorts (p > 0.05). Analyses found small (~5 dB) yet significant differences in aided thresholds between cohorts across most frequencies (p < 0.05). At 1 year post activation, the mean AzBio score for the intact cohort was 68%. Individuals with deactivated electrodes owing to poor sound quality (55%) or incomplete insertion (39%) scored significantly worse than the intact cohort (p < 0.05). Individuals with two or more deactivated electrodes scored significantly worse (48%) than the intact cohort (p < 0.05). Comparison of a subset of individual scores pre-deactivation (32%) and post-deactivation (57%) revealed a significant individual improvement following deactivation (p = 0.02). CONCLUSION: Electrode deactivation is a common occurrence and is associated with higher individual, but lower average group, 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".