Clinical Features Distinguish Eosinophilic and Reflux‐induced Esophagitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Diagnosing eosinophilic esophagitis (EoE) depends on intraepithelial eosinophil count of ≥15 eosinophils per high-power field (HPF); however, differentiating EoE from gastroesophageal reflux disease (GERD) continues to be a challenge because no true "criterion standard" criteria exist. Identifying clinical and endoscopic characteristics that distinguish EoE could provide a more comprehensive diagnostic strategy than the present criteria. The aim of the study was to determine symptoms and signs that can be used to distinguish EoE from reflux esophagitis. METHODS: Adult and pediatric patients with EoE were identified by present diagnostic guidelines including an esophageal biopsy finding of ≥15 eosinophils/HPF. Patients with GERD were age-matched one to one with patients with EoE. Clinical, endoscopic, and histologic information at the time of diagnosis was obtained from the medical record and compared between pairs by McNemar test. A conditional logistic regression model was created using 6 distinguishing disease characteristics. This model was used to create a nomogram to differentiate EoE from reflux-induced esophagitis. RESULTS: Patients with EoE were 75% men and 68% had a history of atopy. Many aspects of EoE were statistically distinct from GERD when controlling for age. Male sex, dysphagia, history of food impaction, absence of pain/heartburn, linear furrowing, and white papules were the distinguishing variables used to create the logistic regression model and scoring system based on odds ratios. The area under the curve of the receiver-operator characteristic curve for this model was 0.858. CONCLUSIONS: EoE can be distinguished from GERD using a scoring system of clinical and endoscopic features. Prospective studies will be needed to validate this model.
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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 | 0.003 |
| 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 | 0.000 | 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 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".