Minimally Traumatic Cochlear Implant Surgery: Expert Opinion in 2010 and 2020
Bibliographic record
Abstract
This study aimed to discover expert opinion on the surgical techniques and materials most likely to achieve maximum postoperative residual hearing preservation in cochlear implant (CI) surgery and to determine how these opinions have changed since 2010. A previously published questionnaire used in a study published in 2010 was adapted and expanded. The questionnaire was distributed to an international group of experienced CI surgeons. Present results were compared, via descriptive statistics, to those from the 2010 survey. Eighteen surgeons completed the questionnaire. Respondents clearly favored the following: round window insertion, slow array insertion, and the peri- and postoperative use of systematic antibiotics. Insertion depth was regarded as important, and electrode arrays less likely to induce trauma were preferred. The usefulness of dedicated soft-surgery training was also recognized. A lack of agreement was found on whether the middle ear cavity should be flushed with a non-aminoglycoside antibiotic solution or whether a sheath or insertion tube should be used to avoid contaminating the array with blood or bone dust. In conclusion, this paper demonstrates how beliefs about CI soft surgery have changed since 2010 and shows areas of current consensus and disagreement.
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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 | 0.006 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".