Clinical predictors of chronic rhinosinusitis: Do the Canadian clinical practice guidelines for acute and chronic rhinosinusitis predict CT-confirmation of disease?
Bibliographic record
Abstract
BACKGROUND: The diagnosis of chronic rhinosinusitis (CRS) based on clinical presentation alone remains challenging. To improve the accuracy of clinical diagnosis, the Canadian Rhinosinusitis Guidelines recommend the use of specific symptom and endoscopic criteria. Our study objective was to determine whether symptom and endoscopic criteria, as defined by the Canadian Rhinosinusitis Guidelines, accurately predict CT-confirmed CRS diagnosis. METHODS: A retrospective cohort study of 126 patients who underwent CT sinuses based on clinical suspicion of possible CRS. The presence of symptom and endoscopic criteria, as defined by the Canadian Rhinosinusitis Guidelines, were compared between patients with and without a CT-confirmed CRS diagnosis using two-tailed Fisher's exact tests. Positive predictive values and likelihood ratios were determined for each symptom and endoscopic finding. RESULTS: Overall, 56.3% of patients had a CT-confirmed diagnosis of CRS. With the exception of nasal polyps, none of the symptom or endoscopic criteria had a statistically significant correlation with positive CT sinuses. For symptom criteria, positive predictive values ranged from 52.4% to 63.4%; likelihood ratios ranged from 0.85 to 1.34. For endoscopic criteria, positive predictive values and likelihood ratios were 71.4% and 1.94 (edema); 63.0% and 1.32 (discharge); and 92.9% and 10.1 (nasal polyps). 35.2% of patients with CT-confirmed CRS had normal endoscopic exams. CONCLUSION: The Canadian Rhinosinusitis Guidelines' symptom and endoscopic criteria for CRS, with the exception of nasal polyps on endoscopy, do not accurately predict CT-confirmed disease. In addition, a normal endoscopic exam does not rule out CRS.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".