Responsiveness and reliability of the Sinus Control Test in chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: The Sinus Control Test (SCT) is a patient-reported questionnaire designed to help physicians identify sub-optimally controlled chronic rhinosinusitis (CRS). This study measures responsiveness to surgery and reliability of the SCT. METHODOLOGY: Adults meeting diagnostic criteria for CRS were recruited from rhinology clinics at a tertiary academic institution. To measure responsiveness, the SCT was administered at baseline and at least 3 months after surgery to 62 CRS patients. To measure reliability, the SCT was administered at two clinical encounters a maximum of 14 days apart to 22 CRS patients. RESULTS: Total SCT scores significantly improved from baseline to post-operative follow-up, and the distribution of patients with total SCT scores falling into the uncontrolled, partially controlled, and controlled categories before and after surgery were significantly different in the direction of improvement. The SCT met minimum standards for reliability and internal consistency as measured by: test-retest reliability coefficient, intra-class correlation coefficients, and item-total correlations. Cronbach alpha; values with each item deleted were lower than the overall Cronbach alpha. The SCT captures the full range of disease control as measured by floor and ceiling effects. CONCLUSION: The SCT is responsive to surgical intervention and a reliable tool to monitor changes in CRS control levels.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".