Disease burden, diagnosis pathways and treatment patterns in patients with eosinophilic esophagitis: a real-world study
Bibliographic record
Abstract
BACKGROUND: Despite a rising prevalence of eosinophilic esophagitis (EoE), real-world data on current management challenges remain limited. OBJECTIVE: To evaluate demographic and clinical characteristics, treatment patterns, and health outcomes in patients with EoE. METHODS: Physicians from Canada, France, Germany, Spain, and the UK retrospectively reviewed records of patients (aged ≥12 years) who were newly diagnosed with EoE between January 2009 and December 2019. EoE diagnosis was confirmed by esophageal biopsy demonstrating a peak eosinophil count ≥15 eosinophils/high-power field (eos/hpf) within 90 days before or after the documented diagnosis (index). Included patients had ≥1 follow-up endoscopy with biopsy and known eosinophil count within 24 months. Data on demographics, clinical characteristics, treatment, and health outcomes were collected 12-month preindex to the last record entry/death. RESULTS: Overall, 415 patients [mean age (SD): 31.8 (14.6) years] were included. The mean (SD) diagnostic delay from symptom onset was 3.4 (4.8) years [median (range): 1.7 (0.7-4.0)]. The average eosinophil count was 41.4 eos/hpf; 80.2% of patients had endoscopic abnormalities of the esophagus at diagnosis. Post-EoE diagnosis, 77.1% of patients received proton-pump inhibitors, 68.2% swallowed topical corticosteroids, 48.4% made dietary modifications, and 18.8% had esophageal dilation. At diagnosis, 74.5% of patients had dysphagia and 46.0% had heartburn. After diagnosis, 63.5%, 62.2%, and 57.6% did not achieve histologic remission (<15 eos/hpf) at the first, second, and third endoscopy, respectively. CONCLUSION: Patients with EoE experience a substantial disease burden, with a significant delay in diagnosis. Achieving optimal disease control remains an unmet need with conventional nonbiologic therapies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".