Systematic review of outcome measures in randomised controlled trials of pediatric eosinophilic esophagitis (EoE) treatment
Bibliographic record
Abstract
Heterogeneity has been noted in the selection and reporting of disease-specific pediatric outcomes in randomized controlled trials (RCTs) [ 1 ]. The consequence may be invalid results from RCTs, or difficulty in comparing results across trials [ 2 , 3 ]. The primary objective of this systematic review was to assess the heterogeneity of outcome measures selection and reporting in recent pediatric EoE treatment trials. As secondary objectives, we assessed the heterogeneity of disease definition and resolution across studies compared to established concensus guidelines, as well as the evidence for current EoE treatments. We searched MEDLINE, EMBASE, The Cochrane Library, Cochrane Central Register of Controlled Trials (CENTRAL), and CINAHL from the last ten years, including randomized controlled trials of EoE treatment in patients 0-18 years. Two authors independently assessed articles for inclusion. A total of 11 studies met inclusion criteria (Fig 1 ). Numerous outcome measures were selected and reported in these trials, with certain measures, such as esophageal eosinophilia, clinical symptoms, safety, histologic features, and endoscopic features, re-occurring frequently, but not universally. Uptake of consensus-established diagnostic criteria for EoE (FIGER criteria) was 30% in trials published after 2007. Due to the small number and heterogeneity of studies obtained, no conclusions regarding treatment efficacy could be made. Flow Diagram The results of this study confirm the need for universally reported, pediatric-specific, standardized outcome measures in EoE trials. Adherence to standardized disease definitions will enhance the utility of outcome measures. Consistent disease definition and standardized outcome reporting will allow for meta-analyses across similar trials and thus inform future clinical decision-making in pediatric EoE.
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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 | 0.071 | 0.257 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.016 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".