Depth of Cochlear Implant Array Within the Cochlea and Performance Outcome
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether the depth of cochlear implant array within the cochlea affects performance outcomes 1 year following cochlear implantation. METHODS: A retrospective case review of 120 patients who were implanted with the Advanced Bionics HiFocus 1J. Post-implantation plain-radiographs were retrospectively reviewed, and the depth of insertion was measured in degrees from the round window to the electrode tip. Correlation between the depth of insertion and 1-year post-activation Hearing in Noise Test (HINT) scores was analyzed. Intrascala position was not assessed. RESULTS: Depth of electrode insertion ranged from 180° to 720°, and HINT scores ranged from 0% to 100%. A Mann-Whitney U test demonstrated significantly improved 1-year post-activation HINT scores in patients with an insertion depth of 360° or more in comparison with patients with insertion depth of less than 360° (81% vs 61%, P = .048). Patients with 13 to 15 contacts within cochlear turns performed as well as patients with full insertion of all 16 contacts, while patients with only 12 contacts performed poorly. CONCLUSIONS: Insertion depth of the AB HiFocus 1J electrode of less than 360° is associated with reduced 1-year post-activation HINT scores when compared with deeper insertions. Partial insertion of 13 active contacts or more led to similar results as full insertion.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".