Sinonasal Outcomes Test‐22 as a Tool to Identify Chronic Rhinosinusitis among Adults with Cystic Fibrosis
Bibliographic record
Abstract
Objectives: (1) Investigate the ability of a sinus‐specific health‐related quality of life questionnaire (HRQoL) to distinguish clinically significant chronic rhinosinusitis (CRS) among adults with cystic fibrosis (CF). (2) Determine an appropriate cutoff score on the Sinonasal Outcomes Test‐22 (SNOT‐22) with sufficient test sensitivity and specificity, to assist caregivers in identifying adults with CF who may warrant specialist referral and treatment. Methods: Participants were enrolled at an adult‐specific CF clinic in a tertiary academic hospital in Vancouver, Canada. Subjects completed the SNOT‐22 followed by endoscopic assessment by otolaryngologists. The Canadian Clinical Practice Guidelines for Chronic Rhinosinusitis were used to confirm diagnosis of CRS. Results: To date, 52 of 80 individuals with a confirmed diagnosis of CF have participated in this study. Thirty‐nine (75.0%) individuals were identified with CRS, 12 (30.8%) of whom presented with nasal polyposis. Aggregate SNOT‐22 scores were significantly higher among individuals with CRS compared to non‐CRS counterparts (39.4 ± 20.0 vs 22.7 ± 8.7, P =. 007, 95% confidence interval [CI] for mean difference: 4.7, 28.7). A SNOT‐22 score >26 was found to have a test sensitivity of 74.4% and specificity of 66.7% for diagnosis of CRS (AUC = 0.77, P <. 01). Using SNOT‐22 scores related to rhinological symptoms increased the likelihood ratio of a positive test when compared to aggregate scores (8.3 vs 2.2, respectively). Conclusions: The SNOT‐22 significantly discriminates between CF adults with and CF adults without CRS. Using rhinological symptom scores increases the likelihood of detecting true CRS cases. The use of this questionnaire may assist specialists in identifying individuals who have clinically significant CRS, warranting specialist referral and treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".