Analyzing the 22‐item Sino‐Nasal Outcome Test using item response theory
Bibliographic record
Abstract
BACKGROUND: The 22-item Sino-Nasal Outcome Test (SNOT-22) is a widely applied patient-reported outcome instrument used to assess the severity of symptoms associated with chronic rhinosinusitis. The purpose of this study was to evaluate the measurement performance of the SNOT-22 instrument on an item-level basis, in a sample of patients awaiting elective surgery for chronic rhinosinusitis. METHODS: This study involved secondary analysis of SNOT-22 data that was prospectively collected from patients diagnosed with chronic rhinosinusitis and awaiting endoscopic sinus surgery in Vancouver, Canada. This study used classic test theory and a 2-parameter graded-response model to evaluate the SNOT-22 items' abilities to measure the severity of chronic rhinosinusitis in terms of patients' self-reported symptoms. This approach models each item's discriminability and difficulty, which provides insight into how well they respectively measure symptoms related to chronic rhinosinusitis. RESULTS: Factor analyses indicated that there are 5 domains of measurement in the SNOT-22. The majority of items demonstrated strong discriminability between symptom severities. Likewise, most of the items demonstrated strong difficulty measuring the symptoms across their range of levels. The exception was those items related to psychological symptoms. Differential item functioning demonstrated that very few of the SNOT-22 items were answered significantly differently by gender or age subgroups. CONCLUSION: This item-level analysis demonstrates that, in general, the SNOT-22 is a strong instrument. Items related to psychological symptoms require further investigation and warrant a supplemental patient-reported outcome instrument.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".