Are statins effective in preventing chronic rhinosinusitis? A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objectives To evaluate the association between statin use and chronic rhinosinusitis (CRS). Design and Setting Systematic review and meta‐analysis. The methodological quality of studies was assessed using the Newcastle–Ottawa scale. Participants Patients with CRS. Main Outcome Measures Pooled odds ratios (ORs) with 95% confidence interval (CIs) in analyses of studies that compared the prevalence of CRS, nasal polyp, difference of Lund‐Kennedy endoscopic score, Lund‐Mackay CT score and Sino‐nasal Outcome Test‐22. Results The analysis included eight studies and 445 465 patients. Patients who used statins were at lower risk for CRS than those who did not (OR = 0.7457, 95% CI = 0.6629–0.8388, p < 0.0001, I2 = 0.0%). Patients with hyperlipidaemia were at higher risk for CRS than those with normal serum levels of lipid (OR = 1.3590, 95% CI = 1.2831–1.4394, p < 0.0001, I2 = 33.3%). However, there were no significant differences in the risk for nasal polyps between CRS patients using statins or not (OR = 1.0931, 95% CI = 0.7860–1.5202, p = 0.5968, I2 = 0.0%). Additionally, statin use was not related to Lund‐Kennedy endoscopic scores, Lund‐Mackay CT scores or sino‐nasal outcome test‐22 scores in CRS patients. Conclusion The risk for CRS is lower in patients who use statins and those without hyperlipidaemia.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".