Incidence and Prevalence of Eosinophilic Esophagitis in Children
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to conduct a systematic review with meta-analysis on the epidemiology of eosinophilic esophagitis (EoE) in children. METHODS: Studies investigating incidence and prevalence of EoE in children (≤ 18 years) were identified in a systematic review of MEDLINE (1950-2011) and Embase (1980-2011). Meta-analyses were performed for incidence and subgroups with ≥ 5 studies: esophagogastroduodenoscopy (EGD) for any indication, histologic esophageal disease, and celiac disease, and EGD for abdominal pain. We used a random effects model, Q statistic to assess heterogeneity, and joinpoint analysis to assess time trends. RESULTS: We included 25 studies. The incidence of EoE varied from 0.7 to 10/100,000 per person-year and the prevalence ranged from 0.2 to 43/100,000. The incidence and prevalence increased over time. Prevalence was highest in children with food impaction or dysphagia (63%-88%). The pooled prevalence was 3.7% (95% confidence interval [CI] 2.4-5.1) in EGD for any indication, 24% (95% CI 19-28) in histologic esophageal disease, 2.3% (95% CI 1.0-3.6) in celiac disease, and 2.6% (95% CI 1.2-4.1) in EGD for abdominal pain. CONCLUSIONS: During the last 2 decades, the incidence and prevalence of EoE in children have increased significantly; however, the population-based incidence and prevalence of EoE vary widely across geographic variations, potentially because of variations in case of ascertainment between centers. Because EoE is common among children with food impaction and dysphagia, children with this presenting complaint should be rapidly identified at triage for timely endoscopic assessment.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.020 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".