Establishing utility values for the 22‐item Sino‐Nasal Outcome Test (SNOT‐22) using a crosswalk to the EuroQol–five‐dimensional questionnaire–three‐level version (EQ‐5D‐3L)
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is a common condition for which there are numerous medical and surgical treatments. The 22-item Sino-Nasal Outcome Test (SNOT-22) is a patient-reported outcome measure often used with patients diagnosed with CRS. However, there are no utility values associated with the SNOT-22, limiting its use in comparative effectiveness research. The purpose of this study was to establish utilities for the SNOT-22 by mapping responses to utility values associated with the EuroQol-5-dimensional questionnaire-3-level version (EQ-5D-3L). METHODS: This study used data collected from patients diagnosed with CRS awaiting bilateral endoscopic sinus surgery in Vancouver, Canada. Study participants completed both the SNOT-22 and the EQ-5D-3L. Ordinary least squares was used for 3 models that estimated the EQ-5D-3L utility values as a function of the SNOT-22 items. RESULTS: for the 3 models ranged from 0.28 to 0.33, and root mean squared errors between 0.23 and 0.24. A nonparametric bootstrap analysis demonstrated robustness of the findings. CONCLUSION: This study successfully developed a mapping model to associate utility values with responses to the SNOT-22. This model could be used to conduct comparative effectiveness research in CRS to evaluate the various interventions available for treating this condition.
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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 | 0.025 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".