Contemporary management of chronic rhinosinusitis with nasal polyposis in aspirin‐exacerbated respiratory disease: an evidence‐based review with recommendations
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) in aspirin-exacerbated respiratory disease (AERD) represents a recalcitrant form of sinonasal inflammation for which a multidisciplinary consensus on patient management has not been reached. Several medical interventions have been investigated, 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 multidisciplinary management of CRS in AERD. 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 included: adult population >18 years old; CRS based on published diagnostic criteria, and a presumptive diagnosis of AERD. We focused on reporting higher-quality studies (level 2 or higher) when available, but reported lower-quality studies if the topic contained insufficient evidence. Treatment recommendations were based on American Academy of Otolaryngology (AAO) guidelines, with defined grades of evidence and evaluation of research quality and risk/benefits associated with each treatment. RESULTS: This review identified and evaluated the literature on 3 treatment strategies for CRS in AERD: dietary salicylate avoidance, leukotriene modification, and desensitization with daily aspirin therapy. CONCLUSION: Based on the available evidence, dietary salicylate avoidance and leukotriene-modifying drugs are options following appropriate treatment with nasal corticosteroids and saline irrigation. Desensitization with daily aspirin therapy is recommended following revision endoscopic sinus surgery (ESS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".