International Consensus Statements on Intraoperative Testing for Cochlear Implantation Surgery
Bibliographic record
Abstract
OBJECTIVES: A wide variety of intraoperative tests are available in cochlear implantation. However, no consensus exists on which tests constitute the minimum necessary battery. We assembled an international panel of clinical experts to develop, refine, and vote upon a set of core consensus statements. DESIGN: A literature review was used to identify intraoperative tests currently used in the field and draft a set of provisional statements. For statement evaluation and refinement, we used a modified Delphi consensus panel structure. Multiple interactive rounds of voting, evaluation, and feedback were conducted to achieve convergence. RESULTS: Twenty-nine provisional statements were included in the original draft. In the first voting round, consensus was reached on 15 statements. Of the 14 statements that did not reach consensus, 12 were revised based on feedback provided by the expert practitioners, and 2 were eliminated. In the second voting round, 10 of the 12 revised statements reached a consensus. The two statements which did not achieve consensus were further revised and subjected to a third voting round. However, both statements failed to achieve consensus in the third round. In addition, during the final revision, one more statement was decided to be deleted due to overlap with another modified statement. CONCLUSIONS: A final core set of 24 consensus statements was generated, covering wide areas of intraoperative testing during CI surgery. These statements may provide utility as evidence-based guidelines to improve quality and achieve uniformity of surgical practice.
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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 | 0.308 | 0.358 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".