High Intraepithelial Eosinophil Counts in Esophageal Squamous Epithelium Are Not Specific for Eosinophilic Esophagitis in Adults
Bibliographic record
Abstract
OBJECTIVES: The histologic criterion of >20 eosinophils per high power field (hpf) is presently believed to establish the diagnosis of idiopathic eosinophilic esophagitis (IEE). This is based on data that the number of intraepithelial eosinophils in gastroesophageal reflux disease (GERD) is less than 20/hpf. This study tests this belief. METHODS: Pathology records were searched for patients who had an eosinophil count >20/hpf in an esophageal biopsy. This patient population was biased toward adults with GERD who had routine multilevel biopsies of the esophagus. The clinical, radiological, and manometric data and biopsies were studied. RESULTS: Forty patients out of a total of 3,648 reports examined had an eosinophil count >20/hpf in squamous epithelium of an esophageal biopsy. Analysis of these 40 cases indicated that 6 (15%) patients had IEE, 2 (5%) had coincident IEE and GERD, 28 (70%) had GERD, and 2 (5%) each had achalasia and diverticulum. There was no significant difference among these groups in terms of maximum eosinophil number, biopsy levels with >20 esoinophils/hpf, presence of eosinophilic microabscesses, involvement of surface layers by eosinophils, and severity of basal cell hyperplasia and dilated intercellular spaces. CONCLUSION: All histologic features presently ascribed to IEE can occur in other esophageal diseases, notably GERD. As such, the finding of intraepithelial eosinophilia in any number is not specific for IEE. When a patient with GERD has an esophageal biopsy with an eosinophil count >20/hpf, it does not mean that the patient has IEE.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".