Development and validation of the Sinonasal Endoscopic Score (SiNES) for chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Although there are several endoscopic grading systems for chronic rhinosinusitis (CRS), they are limited in their range and applicability. We developed a SiNonasal Endoscopic Score (SiNES) that builds upon the strengths of previous systems while addressing their limitations. METHODS: The SiNES system was developed by consensus after multiple rounds of guided discussions. Face, content, and convergent validity were investigated. It was validated using an independent sample of 79 CRS individuals from two referral centres from September 2021 to February 2022. Each patient underwent a sinonasal endoscopy and filled PROM questionnaires. Three independent rhinologists graded endoscopic videos using the SiNES and modified Lund-Kennedy (MLK) scores. Inter-rater and test-retest reliability were assessed via the intraclass correlation coefficient (ICC). SiNES and MLK scores were correlated with PROMs using a Spearman correlation and canonical correlation analysis (CCA). RESULTS: The SiNES system evaluates five anatomical spaces regarding edema, discharge, and scarring. Face, content, and convergent validity were deemed satisfactory by the study authors and an independent panel of Otolaryngologists. Inter-rater reliability was excellent for the SiNES and good for the MLK score. Test-retest reliability was excellent for both systems. Total SiNES was correlated with self-reported smell loss. CONCLUSIONS: The SiNES system is an accurate and reliable grading framework applicable to all CRS subtypes. It can be utilized in clinical and research settings and improves upon previously published systems.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".