Eosinophilic Esophagitis: A retrospective analysis of maintenance therapy
Bibliographic record
Abstract
Objective: Eosinophilic Esophagitis (EoE) is a chronic disease that can cause complications when left untreated. However, the actual guidelines do not clearly specify which category of patients would benefit the most from maintenance treatment. The objective of this study was to determine the rate of relapse in our population and to identify the demographic and clinical characteristics of those patients, so that we can establish a long-term strategy to target them. Methods: This was a retrospective cohort study that included all adult patients who had a diagnosis of EoE between 2010 and 2020 at the Sherbrooke University Hospital Centre. Results: 283 patients were included. Our population consists primarily of male (75.6%), young (63.8% under 45 years old) and atopic patients (67.7%). 37.8% had food impaction and 22.5% had a stenosis at diagnosis. In our center, the percentage of significant relapse is 18.4%. Individuals with a higher risk of relapsing were those with poor adherence to treatment (61.5% vs 38.5%; p-value=0.0) and with a more severe presentation of their disease such as esophageal stenosis (29.0% vs 16.0%; p=0.021) or bolus impaction (29.9% vs 11.4%; p=0.0). Conclusion: A relapse rate as high as 18.4% in our population would justify maintenance treatment in most patients. However, our results show that a more severe presentation of the disease leads to more recurrences, so initiating maintenance treatment in this group should be a priority. Improving adherence to EoE treatment should also be a goal to achieve with our interventions. Keywords Esophageal eosinophilic; Therapy; Maintenance; Relapse; Oesophageal disease; Dysphagia; Remission; Oesophageal stenosis
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".