Speech Coding Strategies and Revised Cochlear Implant Candidacy: An Analysis of Post-Implant Performance
Bibliographic record
Abstract
OBJECTIVE: Technological advances in cochlear implant systems on which a sequence of speech coding strategies have been implemented seem to have resulted in improved speech perception. However, changing selection criteria for implantation have coincided with evolving technology and may confound post-implantation speech perception performance. This study compares speech coding strategy with speech perception performance in severe and profound postlingually deafened adults using one of three successive generations of Nucleus Cochlear Implant speech processors (i.e., Mini Speech Processor, Spectra 22, and SPrint) implementing three speech coding strategies (i.e., MPEAK, SPEAK, and Advanced Combination Encoders; Cochlear Corporation, Englewood, CO, U.S.A.). STUDY DESIGN: Four cohorts of patients were retrospectively reviewed. SETTING: Multicenter, tertiary referral cochlear implant programs in Ontario, Canada. METHODS: Four cohorts of patients (n = 139) were identified based on preimplant audiological measures, duration of deafness, device type, and speech coding strategy. Word and sentence recognition scores at 12 months after implantation were compared using MPEAK with SPEAK22 implemented on the Nucleus 22 speech processors (Mini Speech Processor and Spectra22, respectively) and SPEAK24 as well as Advanced Combination Encoders implemented on the Nucleus 24 SPrint processor. RESULTS: Open-set speech recognition batteries revealed significant improvements in word and sentence scores as advancing technology implemented new speech coding strategies. Subgroup analysis of profoundly deafened patients supported this. Analysis of covariance confirmed that the measured differences could not be accounted for by changing selection criteria for implantation. CONCLUSION: Improvements in performance can be attributed to evolving speech coding strategies and speech processors rather than to differences in preimplant candidacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".