Topical therapies in the management of chronic rhinosinusitis: an evidence‐based review with recommendations
Bibliographic record
Abstract
BACKGROUND: Topical therapies have become an integral component in the management plan for chronic rhinosinusitis (CRS). Several topical therapy strategies have been evaluated, but a formal comprehensive evaluation of the evidence has never been performed. The purpose of this article is to provide an evidence-based approach for the utilization of topical therapies in the management of CRS. METHODS: A systematic review of the literature was performed and the guidelines for development of an evidence-based review with recommendations were followed. Study inclusion criteria were: adult population >18 years old; chronic rhinosinusitis (CRS) based on published diagnostic criteria; and clearly defined primary clinical end-point. We focused on reporting higher-quality studies (level 2b or higher), but reported on lower-level studies if the topic contained insufficient evidence. We excluded drug-eluting spacer and stent therapy from this review. RESULTS: This review identified and evaluated the literature on 5 topical therapy strategies for CRS: saline irrigation, topical steroid, topical antibiotic, topical antifungal, and topical alternatives (surfactant, manuka honey, and xylitol irrigations). CONCLUSION: Based on the available evidence, sinonasal saline irrigation and standard topical nasal steroid therapy are recommended in the topical treatment of CRS. Nonstandard (off-label) topical sinonasal steroid therapies can be an option for managing CRS. The evidence recommends against the use of topical antifungal therapy and topical antibiotic therapy delivered using nebulized and spray techniques in routine cases of CRS. There is insufficient clinical research to provide recommendations for alternative therapies or topical antibiotic therapy delivered using other delivery methods (eg, irrigations).
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".