Core Outcome Set for Reporting Results of Cholesteatoma Surgery From International Otology Outcome Group Consensus Study
Bibliographic record
Abstract
HYPOTHESIS: International consensus exists on optimal measures for reporting outcome from cholesteatoma surgery. This consensus can be used to create a core outcome set (COS) for publication standards. BACKGROUND: Systematic reviews show that the quality of published evidence available to inform surgical decision-making in the management of cholesteatoma is limited with inadequate distinction between residual and recurrent cholesteatoma, use of survival analysis or audiometric reporting standards. METHODS: The International Otology Outcome Group (IOOG) followed COS-STAD and COS-STAR guidelines to develop a COS document. A systematic literature review, which included stakeholder consultation, guided outcome selection. Using a Delphi process, IOOG members and the boards of 5 large otological societies refined the document using on-line survey and email. Consensus was defined as ≥85% agreement among participants across 2 survey rounds. Final outcome measures were categorized as principle (mandatory) or suggested (recommended) standards. RESULTS: Principle reporting standards included: distinction of residual from recurrent cholesteatoma, with use of survival analysis for outcome at 5 years, description of technique for detection of residua, use of audiometric reporting standards, and distinction between complications from cholesteatoma and surgery. Suggested standards covered reporting of cholesteatoma severity, surgical nomenclature, and further audiometric measures. Patient-reported outcome measures are recognized as important, but too few responses were received to define an optimal PROM. CONCLUSION: This COS provides a consensus-based framework for standardized reporting in cholesteatoma surgery. Adoption of the COS as a publishing standard should improve the quality of evidence available to guide surgical care.
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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 | 0.363 | 0.531 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.023 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".