Use of the SNOT-22 and UPSIT to Appropriately Select Pediatric Patients With Cystic Fibrosis Who Should Be Referred to an Otolaryngologist
Bibliographic record
Abstract
IMPORTANCE: Sinonasal disease and, specifically, nasal polyps, occur frequently in children with cystic fibrosis (CF). As survival rates have improved, it has become imperative that otolaryngologists become involved in the care of patients with CF to provide appropriate medical and surgical interventions for sinonasal disease. Despite significant variability in the subjective reporting of clinical symptoms, previous work has suggested there may be a relationship between clinical indicators and sinonasal disease in this population. OBJECTIVE: To determine whether the 22-item Sino-Nasal Outcome Test (SNOT-22), the University of Pennsylvania Smell Identification Test (UPSIT), and other measures of sinonasal disease could be used to predict the presence of subclinical nasal polyps in children with CF. DESIGN, SETTING, AND PARTICIPANTS: This was a cross-sectional study performed from May 2012 through April 2013 at a cystic fibrosis clinic at BC Children's Hospital in Vancouver, British Columbia, Canada. There were 72 eligible children with CF for this study (with a confirmed diagnosis of CF based on genetic testing; their ages ranged from 6 to 18 years, and they were not actively being treated by an otolaryngologist). Thirty-seven of these patients (23 males, 14 females) consented to participate in this study. Twenty-three declined participation, and 12 could not be contacted. MAIN OUTCOMES AND MEASURES: Potential clinical predictors for the presence of subclinical nasal polyps were determined a priori. All 37 recruited participants completed a full study assessment. Nasal endoscopy (the gold standard) was performed to determine the presence of nasal polyps. Potential predictors that were assessed included age, sex, genotype, pancreatic function, SNOT-22 and UPSIT scores, oral culture swab result, and severity of forced expiratory volume in 1 second (FEV(1)). RESULTS: A SNOT-22 score of greater than 11 was the only statistically significant predictor of nasal polyps (P = .04). The positive predictive value was 68.1%, the negative predictive value was 66.7%, and the positive likelihood ratio was 1.82. CONCLUSIONS AND RELEVANCE: Given that the SNOT-22 is easy to administer and inexpensive, this sinus disease-specific questionnaire seems to be an appropriate tool for routine use by respirologists when assessing patients with CF to help predict subclinical nasal polyps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".