Eosinophilic esophagitis: absolute eosinophilic count, peak eosinophilic count, and potential biomarkers of eosinophilic degranulation products—an in-depth systematic review
Bibliographic record
Abstract
Background: eosinophils (DGE/DGE + NDGE: degranulated eosinophils/degranulated eosinophils and non-degranulated eosinophils). Methods: This is the first in-depth systematic review study using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) parameters involving a literature search of academic databases (PubMed, Scopus, Medline, Google Scholar, and Cochrane Database, 2011-2022) targeting specifically the eosinophilic counts and ratio, and the eosinophilic degranulation products as potential biomarkers. Data were extracted from ten selected studies and presented on a spreadsheet. Results: eosinophils. Conclusions: A few minimally invasive methods and biomarkers may be suggested as alternative tools in diagnosing and monitoring eosinophilic esophagitis. While there is no consensus on the clinical usefulness of these biomarkers, our critical evaluation may suggest that the eosinophilic degranulation ratio (DGE/DGE + NDGE: degranulated eosinophils/degranulated eosinophils and non-degranulated eosinophils) in the esophagus may be critical for evaluating properly these biomarkers. An increasing trend may culminate in the potential clinical use of these biomarkers evaluated not only with the peak eosinophilic count, but also with the degranulation score upon regulatory bodies' approval to monitor eosinophilic esophagitis in the future. We strongly advocate for the necessity to score the esophageal biopsies with both a peak eosinophilic count and a score of the degranulated eosinophils.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".