Association of Speech Processor Technology and Speech Recognition Outcomes in Adult Cochlear Implant Users
Bibliographic record
Abstract
OBJECTIVE: Determine association of advancements in speech processor technology with improvements in speech recognition outcomes. STUDY DESIGN: Retrospective cohort. SETTING: Tertiary referral center. PATIENTS: Adult unilateral cochlear implant (CI) recipients. INTERVENTION: Increasing novelty of speech processor defined by year of market availability. MAIN OUTCOME MEASURES: Consonant-Nucleus-Consonant (CNC) and Hearing in Noise Test (HINT) in quiet. RESULTS: From 1991 to 2016, 1,111 CNC scores and 1,121 HINT scores were collected from 351 patients who had complete data. Mean post-implantation CNC score was 53.8% and increased with more recent era of implantation (p < 0.001, analysis of variance [ANOVA]). Median HINT score was 87.0% and did not significantly vary with implantation era (p = 0.06, ANOVA). Multivariable generalized linear models were fitted to estimate the effect of speech processor novelty on CNC and HINT scores, each accounting for clustering of scores within patients and characteristics known to influence speech recognition outcomes. Each 5-year increment in speech processor novelty was independently associated with an increase in CNC score by 2.85% (95% confidence limits [CL] 0.26, 5.44%) and was not associated with change in HINT scores (p = 0.30). CONCLUSION: Newer speech processors are associated with improved CNC scores independent of the year of device implantation and expanding candidacy criteria. The lack of association with HINT scores can be attributed to a ceiling effect, suggesting that HINT in quiet may not be an informative test of speech recognition in the modern CI recipient. The implications of these findings with respect to appropriate interval of speech processor upgrades are discussed.
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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 | 0.000 |
| 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".