Qualitative development of the sinus control test: a survey evaluating sinus symptom control
Bibliographic record
Abstract
BACKGROUND: Ideal management of chronic rhinosinusitis (CRS) requires ongoing monitoring of disease and its control. Existing control instruments are limited in their correlation to patient reported outcomes, the need for endoscopy, or lack of validation from a multidisciplinary group. The goal of this study was to develop a patient-based Sinus Control Test (SCT) for determining CRS control. METHODS: A systematic literature review and focus groups consisting of 20 patients and 11 medical experts in CRS from various medical specialties were used to generate items. A draft 13-item questionnaire was administered to 50 patients with CRS in a prospective fashion. Patients were evaluated using the 22-item Sino-Nasal Outcome Test (SNOT-22) instrument, Lund-Mackay computed tomography (CT) score, and Lund-Kennedy endoscopy score. A rhinologist blinded to the questionnaire results also provided an overall control of the disease for each patient. A regression model was generated to identify which subset of items showed the greatest discriminate ability in relation to specialist's and patient's global rating of disease control. RESULTS: Four questions were included in the final questionnaire (p < 0.05), each with a scale of 0 to 4, with an overall total score ranging from 0 to 16. Optimal classification resulted in patients with a score from 1 to 3 (well controlled), 4 to 11 (partially controlled), and 12 to 16 (uncontrolled). SCT scores correctly classified control levels 72% of the time when compared to physician's assessment. CONCLUSION: The SCT is a simple, patient generated questionnaire that can measure the control of CRS without requirement of endoscopy or CT evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".