High tissue eosinophilia as a marker to predict recurrence for eosinophilic chronic rhinosinusitis: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Patients with eosinophilic chronic rhinosinusitis (ECRS) have been shown to have greater disease severity and poorer treatment outcomes after sinus surgery. Although the inflammatory pattern of ECRS is essential to diagnosing this subtype, there is currently no consensus for diagnosis. Our aim in this study was to determine whether high tissue eosinophilia (HTE), measured as eosinophils per high-power field (eos/HPF), could be used to define ECRS based on likelihood of recurrence. METHODS: Embase, Medline, and PubMed databases were searched for studies that reported HTE and recurrence in ECRS patients after surgical treatment. We used a random-effects bivariate meta-analysis to calculate summary sensitivity, specificity, and diagnostic odds ratios (DORs) for detecting ECRS at different HTE cut-off scores using risk of recurrence as the primary outcome. RESULTS: We identified 11 articles (n = 3183) that reported HTE associated with recurrence. A cut-off value of >55 eos/HPF showed the highest sensitivity (0.87; 95% confidence interval [CI], 0.82-0.91), specificity (0.97; 95% CI, 0.93-0.99), and DOR (232.7; 95% CI, 91.0-595.1). Meta-regression analysis performed showed that the Quality Assessment of Diagnostic Accuracy Studies score (p = 0.1287), geographic location (p = 0.3745), follow-up time (p = 0.2879), and study design (p = 0.1865) did not affect the test accuracy. CONCLUSION: Our findings suggest that using eos/HPF has good diagnostic accuracy and may be a useful tool for identifying ECRS patients. Based on the results of our meta-analysis, we recommend a cut-off value of >55 eos/HPF for predicting the likelihood of recurrence of ECRS.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".