Recommendations for the diagnosis and management of eosinophilic esophagitis in adults and children in Canada: a Delphi consensus project
Bibliographic record
Abstract
Background: Eosinophilic esophagitis (EoE) is a chronic inflammatory disease of the esophagus that effects both pediatrics and adult patients in Canada and is increasing in prevalence. No Canadian focused best practice recommendations currently exist to guide clinical practice. Methods: The study used a modified Delphi technique to develop evidence and expert opinion-based recommendations for providing care for patients with EoE. The Delphi process consisted of 3 rounds of quantitative surveys and qualitative consensus meetings. Experts were included in the Delphi if they had experience caring for EoE patients in Canada within one of the following professional groups: allergist, adult gastroenterologists, pathologists, pediatric gastroenterologists, and dieticians. Results: Delphi rounds were completed between May 1, 2024, and June 30, 2024. A total of 31 experts in EoE care from across Canada were recruited to participate in the Delphi consensus process. All participants completed all 3 rounds of Delphi surveys. The final statement includes 38 recommendations for the care of patients with EoE organized into 3 sections: definition, diagnosis, and management. A table of research gaps is provided to stimulate further knowledge development on this topic. Conclusion: This consensus statement includes actionable recommendations to support quality care of patients with EoE at any age across Canada. We encourage EoE centres in Canada to come together in a multidisciplinary form to not only provide clinical care but also do much needed research on Canadian specific topics and gaps in EoE care.
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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 | 0.207 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".