Speech perception performance for 100 post-lingually deaf adults fitted with Neurelec cochlear implants: Comparison between Digisonic<sup>®</sup>Convex and Digisonic<sup>®</sup>SP devices after a 1-year follow-up
Bibliographic record
Abstract
CONCLUSION: Patients implanted with the Digisonic® SP device showed better identification scores than those implanted with the Convex device, with skills continuing to improve over a longer time period. Technological improvements were beneficial in terms of speech perception in quiet. OBJECTIVE: To compare speech perception skills for post-lingually deaf patients implanted with a previous Neurelec device, the Digisonic® Convex, with those implanted with a more recent one, the Digisonic® SP, which provides more electrodes and a faster stimulation rate. METHODS: This was a retrospective study of 100 implanted patients, 45 with the Digisonic® Convex implant and 55 with the Digisonic® SP. Speech perception (dissyllabic words and sentences, in open set) was evaluated until 1 year after implantation. RESULTS: Patients fitted with the Digisonic® SP implant showed significantly better scores after 3, 6, and 12 months (mean scores: 53%, 62%, and 68% for words; 58%, 69%, and 75% for sentences) than those fitted with the Convex implant (34%, 42%, and 43% for words; 38%, 59%, and 51% for sentences). The improvement in speech perception after implantation for SP patients continued throughout the 12 months for words and 6 months for sentences, versus 6 months for words and 3 months for sentences for Convex patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".