The prevalence of Eustachian tube dysfunction symptoms in patients with chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: While Eustachian tube dysfunction (ETD) is a known comorbidity of chronic rhinosinusitis (CRS), the prevalence of ETD symptoms in the CRS population is poorly understood. We sought to determine the cross-sectional prevalence of ETD in patients with CRS using the validated Eustachian Tube Dysfunction Questionnaire (ETDQ-7) and to correlate ETDQ-7 scores with 22-item Sino-Nasal Outcome Test (SNOT-22) scores, endoscopy scores, and computed tomography (CT) scores. METHODS: A total of 101 patients with confirmed CRS completed the ETDQ-7 and SNOT-22 at their initial visit to our rhinology clinic. Lund-Mackay CT and Lund-Kennedy endoscopy scores were also obtained. Spearman's correlation coefficient (ρ) was calculated. RESULTS: Among the 101 patients, 49 patients (48.5%) had an ETDQ-7 score of ≥14.5, signifying clinically significant ETD. The mean ± standard deviation (SD) ETDQ-7 score of the entire cohort was 17.8 ± 10.1. There was a moderately strong correlation between ETDQ-7 and the SNOT-22 ear subdomain (ρ = 0.691, p < 0.001). The correlation coefficient between ETDQ-7 and total SNOT-22 scores was ρ = 0.491 (p < 0.001), indicating moderate correlation. ETDQ-7 scores were poorly correlated to objective measures of sinonasal disease, including Lund-Mackay CT score (ρ = -0.055, p = 0.594) and Lund-Kennedy endoscopy score (ρ = -0.099, p = 0.334). CONCLUSION: Symptoms of ETD are highly prevalent among patients with CRS as documented by patient-reported outcome measures. The correlation between ETDQ-7 scores and SNOT-22 ear subdomain scores is moderately strong, while the correlation between ETDQ-7 scores and SNOT-22 scores is moderate. ETD severity does not correlate with CT score or nasal endoscopy score.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".