International Collaborative Assessment of the Validity of the EAONO-JOS Cholesteatoma Staging System
Bibliographic record
Abstract
OBJECTIVE: Assessment of validity of the Japanese Otological Society and the European Academy of Otology and Neurotology (EAONO-JOS) cholesteatoma staging system (EJS) through international collaboration to investigate: (a) feasibility of retrospectively staging previously acquired data, (b) strengths and weaknesses of the staging system. METHOD: Nine centers with prospective cholesteatoma databases were recruited. Cases were allocated to EJS Stage at each center using details from time of initial surgery. An independent rater also staged the cases and noted any discrepancies. At one center results from database staging were compared with staging based on contemporaneous operative records. Inter and intrarater reliabilities were calculated, and recidivism rates calculated according to Stage. RESULTS: Of 1482 cases of cholesteatoma, 320 (22%) were Stage 1, 977 (67%) Stage 2, 153 (11%) Stage 3 and 4 (0.3%) Stage 4. No database contained details of all parameters required for accurate staging. Staging discrepancies occurred in >10% cases but inter and intrarater reliability of staging were high (Kappa 0.8; 95% confidence interval between 0.7-0.9). At 5 years, 11% had residual and 8% had recurrent cholesteatoma: rates increased with Stage but generally not significantly (Kaplan-Meier Log Rank analysis). CONCLUSION: The EJS Staging system provides an internationally agreed standard for collecting data to classify cholesteatoma severity. Although data can be applied retrospectively with reasonable reliability, prospective data collection would prevent inaccuracies that arise from missing data fields. To enhance validity of the EJS system, we propose clearer definitions of some categories. Refinements to definitions of stage may improve prognostic utility of the EJS but should be made using evidence powered by large-scale collaboration.
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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 | 0.071 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".