The use of Postoperative Topical Corticosteroids in Chronic Rhinosinusitis with Nasal Polyps: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Topical intranasal corticosteroids (INCSs) are used to control disease symptoms in patients with chronic rhinosinusitis with nasal polyposis (CRSwNP). The evidence to recommend INCSs as part of the postoperative care is limited. This study was designed to assess the efficacy of INCSs in the postoperative care of patients undergoing functional endoscopic sinus surgery (FESS) during the 1st year postoperatively. METHODS: We searched the Cochrane Central Register of Controlled Trials (1995 to May 2012), MEDLINE (January 1948 to May 2012), EMBASE (January 1980 to May 2012), and the reference lists of articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Randomized controlled trials (RCT) and cohort studies comparing INCSs with placebo or comparing different types of INCSs were included. RESULTS: Eleven studies (n = 945 patients) were RCTs and one prospective cohort study (n = 32 patients). As measured by the standardized mean difference (SMD) INCSs had a beneficial effect on symptom scores (SMD, -1.35; 95% CI, -2.05 to -0.64; p = 0.0002; 3 trials; 137 patients) and polyp score (SMD, 0.53; 95% CI, -0.91 to -0.14; p = 0.007; 5 trials; 223 patients). Compared with placebo, the use of INCSs decreased the odds of polyp recurrence (odds ratio, 0.17; 95% CI, 0.06-0.51; p = 0.002; 2 trials; 74 patients). Two RCTs (n = 105) and one cohort study (n = 32) reported normal adrenocorticotropic hormone levels postintervention. CONCLUSION: INCS use is a safe therapy in postoperative management of CRSwNP patients. INCS showed significant improvement in polyp score, patients' symptoms and significant decrease in polyp recurrence in the first year postoperatively.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".