Low 22‐item sinonasal outcome test scores in chronic rhinosinusitis: Why do patients seek treatment?
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Patients with chronic rhinosinusitis (CRS) who experience minimal reductions in quality of life (QoL) may present for treatment despite QoL scores comparable to controls without CRS. This study seeks to identify cofactors influencing patients with CRS and low 22-item Sinonasal Outcome Test (SNOT-22) scores to seek care. STUDY DESIGN: Prospective, multicenter, observational cohort. METHODS: Patients with CRS were enrolled between April 2011 and September 2015. Patients with sinonasal mucocele or unilateral sinus opacification were excluded. Control subjects without CRS were enrolled for comparison. Low-SNOT CRS was defined as a SNOT-22 score < 20. RESULTS: A total of 774 subjects (low-SNOT CRS, n = 38; high-SNOT CRS, SNOT-22 ≥ 20, n = 641; controls without CRS, n = 95) were enrolled. Low SNOT scores were identified in 6% of subjects with CRS. After adjustment, low-SNOT CRS and control groups without CRS reported similar baseline average SNOT-22 total scores (P = .879). Unexpectedly, compared to controls, low-SNOT CRS patients had significantly better average psychological (2.1 ± 2.3 vs. 5.8 ± 6.0; P = .030) and sleep dysfunction (2.7 ± 3.4 vs. 6.0 ± 5.2; P = .016) scores. Fourteen of 38 (37%) low-SNOT patients elected to undergo endoscopic sinus surgery (ESS), with a significantly lower likelihood of reporting a minimal clinically important difference (MCID) when compared to high-SNOT patients (43% vs. 82%; P < .001) after a mean follow-up of ∼15 months. CONCLUSIONS: Low-SNOT CRS patients represent an outlier population for which measures of QoL fail to identify factors influencing the decision to seek treatment. Low-SNOT CRS patients electing ESS have a decreased likelihood of reporting MCIDs following ESS. Further study is required to identify novel factors associated with treatment-seeking behavior in this population. LEVEL OF EVIDENCE: 3B Laryngoscope, 127:22-28, 2017.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".