High tissue eosinophilia as a marker to predict recurrence for eosinophilic chronic rhinosinusitis: a systematic review and meta‐analysis
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it