Measuring control of disease in Chronic Rhinosinusitis; assessing the correlation between SinoNasal Outcome Test-22 and Visual Analogue Scale item scores
Bibliographic record
Abstract
BACKGROUND: In chronic rhinosinusitis (CRS), aim of treatment is control of disease. EPOS2020 suggests the use of visual analogue scale (VAS) measurements on several symptoms. We aim to determine if individual VAS items can be replaced by widely used SinoNasal Outcome Test-22 (SNOT-22) items when determining control of disease, to avoid using double measurements and to stimulate its use in clinical practice. METHODS: Analyses were made on correlations between individual SNOT-22 scores and symptom-specific questions from consecutive patients with CRS visiting our tertiary referral rhinologic clinic for the first time. RESULTS: 157 CRS patients were included. Correlations of individual items were strong (r greater than 0.8). Best parity in sensitivity, specificity, positive predicting value, negative predicting value, odds ratio and Receiver Operating Characteristic curves were found in individual item score of VAS greater than 5 and SNOT item-score. This cut off is valid for measuring control of disease, combining several nasal, facial pain and sleep symptoms (controlled, partially controlled and uncontrolled). CONCLUSION: There is strong correlation between individual items measured as SNOT or VAS. For the definition of CRS disease control, as proposed in EPOS2020, the use of symptoms specific SNOT 23 is predictive of VAS greater than 5.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".