Medical management of allergic fungal rhinosinusitis following endoscopic sinus surgery: an evidence‐based review and recommendations
Bibliographic record
Abstract
BACKGROUND: Allergic fungal rhinosinusitis (AFRS) is a subset of polypoid chronic rhinosinusitis that is characterized by the presence of eosinophilic mucin with fungal hyphae within the sinuses and a Type I hypersensitivity to fungi. The treatment of AFRS usually involves surgery in combination with medical therapies to keep the disease in a dormant state. However, what constitutes an optimal medical regimen is still controversial. Hence, the purpose of this article is to provide an evidence-based approach for the medical management of AFRS. METHODS: A systemic review of the literature on the medical management of AFRS was performed using Medline, EMBASE, and Cochrane Review Databases up to March 15, 2013. The inclusion criteria were as follows: patients >18 years old; AFRS as defined by Bent and Kuhn; post-sinus surgery; studies with a clearly defined end point to evaluate the effectiveness of medical therapy in postoperative AFRS patients. RESULTS: This review identified and assessed 6 medical modalities for AFRS in the literature: oral steroids; topical steroids; oral antifungals; topical antifungals; immunotherapy; and leukotriene modulators. CONCLUSION: Based on available evidence in the literature, postoperative systemic and standard topical nasal steroids are recommended in the medical management of AFRS. Nonstandard topical nasal steroids, oral antifungals, and immunotherapy are options in cases of refractory AFRS. No recommendations can be provided for topical antifungals and leukotriene modulators due to insufficient clinical research reported in the literature.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".