Clinical efficiency and safety of the oticon medical neuro cochlear implant system: a multicenter prospective longitudinal study
Bibliographic record
Abstract
OBJECTIVE: This prospective longitudinal cohort study at six tertiary referral centers in Canada and Denmark describes the clinical efficiency and surgical safety of cochlear implantation with the Oticon Medical Neuro cochlear implant system, including the Neuro Zti implant, the EVO electrode array, and the Neuro One sound processor. METHODS: Patients were adult cochlear implant candidates with bilateral sensorineural hearing loss. RESULTS: The mean HINT scores in quiet pre-operatively and at 3, 6, and 12 months post-activation were 13%, 58%, 67%, and 72%, respectively, and in noise (+10 dB SNR) 13%, 46%, 53%, and 59%, respectively. The mean improvement from baseline to 6 months post-activation was 54% in quiet and 40% in noise. The surgical major complication incidence rate was 0% and the post-surgical major complication incidence rate (until 12 months post-activation) was 4%. There was no adverse event that was fatal, that required explantation, or that resulted in sound processor nonuse, and no implant failure. CONCLUSION: Cochlear implantation with the Oticon Medical Neuro system enables speech identification both in quiet and in noise and audiologic outcomes continue to improve in the year following activation. No substantial adverse events occurred during the surgical implantation procedure and during the 12 months post-activation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".