Evaluation of the High-Resolution Speech Coding Strategy for the Clarion CII Cochlear Implant System
Bibliographic record
Abstract
Four postlinguistically deafened adults were implanted with the Clarion CII cochlear implant with the HiFocus II electrode in an evaluation of performance with a new speech coding strategy (high resolution) compared with current speech coding strategies (multiple pulsatile sampler, continuous interleaved sampling, and simultaneous analog stimulation). These strategies were implemented in the Platinum speech processor from Advanced Bionics Corporation (Sylmar, CA). Postoperatively, subjects were fitted with the traditional coding strategies and over the first month were allowed to determine their strategy of choice. This strategy was used to evaluate open-set speech recognition performance at 1 month and 3 months postfitting. At 3 months postfitting, subjects were reprogrammed with the high-resolution strategy. They returned for speech recognition testing at 1 month and 3 months postfitting with this strategy. Performance was significantly better with the high-resolution strategy for all four subjects, particularly when listening to speech in background noise. This finding was in agreement with their strong preference for the high-resolution strategy, and all four patients continue to use the high-resolution strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".