Patient‐centered decision making: the role of the baseline SNOT‐22 in predicting outcomes for medical management of chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: For patients with chronic rhinosinusitis (CRS), the decision to elect continued medical management vs surgery is complex and involves tradeoffs between benefits, risks, and overall effectiveness of each therapy. The purpose of this study is to investigate whether baseline disease-specific quality of life (QOL) can assist in predicting outcomes in patients with refractory CRS who elect continued medical management. METHODS: CRS patients electing medical management were enrolled in a prospective, multi-institutional cohort study. Patients were stratified into pretreatment 22-item Sino-Nasal Outcome Test (SNOT-22) subgroups based on 10-point score increments (eg, 10 to 19, 20 to 29, 30 to 39, etc.) to capture potential outcome differences by baseline SNOT-22 disease burden. The proportion of patients achieving minimal clinically important difference (MCID≥9 points) and relative improvement (%) for each score category were calculated. RESULTS: Seventy-five CRS patients with a mean ± standard deviation pretreatment SNOT-22 score of 45.2 ± 16.6 were followed for a mean of 14.9 months. The majority of participants electing medical therapy failed to improve 1 MCID (57%) with a mean relative score improvement of 16%. Overall, 37% of patients maintained baseline SNOT-22 QOL status, whereas 20% of patients deteriorated >1 MCID. When treatment crossover patients (to endoscopic sinus surgery [ESS]) were included (n = 117), approximately 1 in 4 (27%) patients achieved an MCID. CONCLUSION: Results from this study suggest that the majority of CRS patients electing ongoing medical management with low baseline disease-specific QOL impairment maintain stable QOL with continued medical management. Furthermore, of CRS patients electing ongoing medical therapy, approximately 1 in 4 patients achieved MCID, whereas 1 in 5 experienced deterioration by >1 MCID.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".