Comorbidities associated with eosinophilic chronic rhinosinusitis: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Eosinophilic chronic rhinosinusitis (ECRS) is a histological subtype of CRS that is generally recognised as being more difficult to manage. Patients with ECRS tend to have greater disease severity and poorer treatment outcomes after sinus surgery when compared with non-ECRS patients. The histopathology and biomarker assessments of ECRS are often unavailable prior to surgery and may be impractical and costly to analyse. Thus, the primary objective of this study was to understand clinical comorbidities associated with ECRS. DESIGN/SETTING: We searched three independent databases for articles that reported clinical CRS comorbidities associated with tissue eosinophilia. Data from studies with the same reported comorbidities were pooled, and a forest plot analysis was used to assess potential associations with four different conditions including allergic rhinitis, ASA sensitivity, asthma and atopy. The association between the phenotype of nasal polyps and ECRS was also quantified as a secondary objective. ECRS cut-off levels were as defined by papers included. MAIN OUTCOME/RESULTS: Eighteen articles were identified. The presence of nasal polyps (the first numbers in brackets represent odds ratios) (5.85, 95% CI [3.61, 9.49], P < .00001), ASA sensitivity (5.63, 95% CI [3.43, 9.23], P < .00001), allergic rhinitis (1.84, 95% CI [1.27, 2.67], P = .001) and asthma (3.15, 95% CI [2.61, 3.82], P < .00001) were found to be significantly associated with tissue eosinophilia. Atopy, however, was not significantly associated with tissue eosinophilia (1.71, 95% CI [0.59, 4.95], P = .32). CONCLUSION: Certain clinical disease characteristics such as ASA sensitivity, allergic rhinitis and asthma are more associated with CRS patients with eosinophilia when compared to those without eosinophilia. The phenotype of nasal polyps was also associated with ECRS. It is important for surgeons to recognise these comorbidities to ensure correct diagnoses, management and follow-up are implemented.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.024 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".