The Sino‐Nasal Outcome Test–22 as a tool to identify chronic rhinosinusitis in adults with cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is becoming increasingly prevalent in adults with cystic fibrosis (CF), as the median age of survival rises for these individuals. Delayed identification of CRS may contribute to worsening health-related quality of life and increased treatment burden. Our objective was to investigate the utility of the 22-item Sino-Nasal Outcome Test (SNOT-22) as a tool to identify CRS in adults with CF. METHODS: In this cross-sectional study, participants were sampled from an adult-specific CF clinic in Vancouver, Canada, between September 2013 and April 2014. CRS was determined by use of standardized diagnostic guidelines. Participants completed the SNOT-22 and medical charts were reviewed for additional predictor variables. Logistic regression was used to compare the SNOT-22 as a univariable predictor variable to a multivariable prediction model, in order to best differentiate CRS and non-CRS participants. RESULTS: Ninety-three of 101 adults provided written informed consent. The prevalence of CRS was 56.3% (95% confidence interval [CI], 45.9% to 66.3%). Individuals with CRS reported significantly higher SNOT-22 scores than non-CRS participants (mean difference: 13.9; 95% CI, 6.1 to 21.7). The optimal SNOT-22 score to differentiate CRS was 21 out of 110 (sensitivity: 76%, specificity: 61%, positive predictive value: 71%, likelihood ratio: 1.9). CONCLUSION: Compared to the current diagnostic gold standard, SNOT-22 scores greater than 21 sufficiently identified adults with CF presenting with concomitant CRS. The SNOT-22 is a simple instrument that can easily be implemented in adult CF clinics to assist care providers identify individuals requiring more detailed assessment or referral to a sinus clinic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".