Aspirin desensitization therapy in aspirin‐exacerbated respiratory disease: a systematic review
Bibliographic record
Abstract
BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) represents an aggressive form of chronic rhinosinusitis with nasal polyposis that is notoriously challenging to treat. There is evidence to suggest desensitization to aspirin may improve symptomatology and disease control in these patients. The goal of our study was to critically appraise the literature on this topic and assess the effect of desensitization on sinonasal symptomatology. METHODS: We searched EMBASE, CINAHL, MEDLINE, and the Cochrane Library for relevant literature. Studies were included if they were observational studies or randomized, controlled trials, had n > 1, and were published in English or French. Studies were excluded if they were systematic reviews. We assessed study for quality and presence of common sources of bias. RESULTS: Twenty-four studies met the inclusion criteria. In general, polyp size, polyp recurrence, nasal symptom scores, sense of smell, number of acute rhinosinusitis episodes, and systemic steroid use improved when patients were desensitized. The vast majority of studies recommend desensitization. CONCLUSION: There is mounting evidence that aspirin desensitization is a valuable adjunct to treat sinonasal symptoms in the treatment of patients who have AERD.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".