Stratification of SNOT-22 scores into mild, moderate or severe and relationship with other subjective instruments
Bibliographic record
Abstract
AIMS AND OBJECTIVES: The European Position Paper on Rhinosinusitis and Nasal Polyps provides treatment algorithms based on the mild/moderate/severe (MMS) classification. To date there has been no statistically validated stratification of the SNOT-22 score according to this classification. METHODS: 65 consecutive patients diagnosed with CRS completed a SNOT-22, VAS and rated their symptoms according to MMS and impact on quality of life. RESULTS: The median SNOT 22 scores varied between the 3 MMS categories. The interquartile ranges for the respective MMS groups were: Mild 8-17, Moderate 22.5-48, Severe 54-83. Median values for the respective MMs groups were: Mild 12, Moderate 36 and Severe 66. 15.38% of patients in the Mild category, 95.24% in the Moderate category and 100% in the Severe category feel their QoL is affected. There was a strongly positive correlation between the SNOT-22 and VAS scores. CONCLUSION: We propose a statistically validated definition for stratification of the SNOT-22, with Mild being defined on the SNOT-22 score as 8-20 inclusive, Moderate as >20-50 and Severe as >50.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".