Dupilumab Efficacy in Children With Eosinophilic Esophagitis With Prior Swallowed Topical Corticosteroid Use: A Subgroup Analysis
Bibliographic record
Abstract
INTRODUCTION: Swallowed topical corticosteroids (STCs) are commonly used to treat eosinophilic esophagitis (EoE); however, not all patients respond and others may be intolerant or have contraindications. We assessed dupilumab efficacy in children with EoE with prior swallowed topical corticosteroids (STC) use and with prior inadequate response, intolerance, and/or contraindication (IRIC) to STCs in the phase 3 EoE KIDS study. METHODS: Eligible patients were aged 1-11 years with EoE unresponsive to proton-pump inhibitors. In part A, patients were randomized to weight-tiered higher-exposure or lower-exposure dupilumab, or placebo up to week (W)16. In part B, dupilumab groups continued treatment, whereas patients receiving placebo switched to higher-exposure or lower-exposure dupilumab through W52. Efficacy by prior STC status was assessed at W16 and W52. RESULTS: Of 102 patients, 82 (80%) received prior STCs and 59 (58%) had prior IRIC to STCs. At W16, higher-exposure dupilumab improved rates of histologic remission vs placebo in patients with prior STC use (60.7% vs 0.0%, nominal P < 0.0001) and prior IRIC to STCs (60.9% vs 0.0%, nominal P < 0.0001). Secondary endoscopic and histologic outcomes were similar. Responses were maintained at W52 with higher-exposure dupilumab, with improvements observed in patients who switched from placebo to higher-exposure dupilumab. Results were similar or numerically lower with lower-exposure dupilumab. Findings seemed comparable in those without prior STC use or prior IRIC, although patient numbers were small. Dupilumab safety was consistent with the known safety profile. DISCUSSION: Dupilumab may be an effective treatment in children with EoE with prior STC use or prior IRIC to STCs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".