A Clinician's Guide to Gluten Challenge
Bibliographic record
Abstract
Gluten challenge is an essential clinical tool that involves reintroducing or increasing the amount of gluten in the diet to facilitate diagnostic testing in celiac disease (CD). Nevertheless, there is no consensus regarding the applications of gluten timing, dosing, and duration in children. This review aims to summarize the current evidence, discuss practical considerations, and proposes a clinical algorithm to help guide testing in pediatric patients. Childhood development, social circumstances, and long-term health concerns must be considered when identifying a candidate for gluten challenge. Based on previous studies, the authors suggest baseline serology followed by a minimum of 3-6 grams of gluten per day for over 12 weeks to optimize diagnostic accuracy for evaluation of CD. A formal provider check-in at 4-6 weeks is essential so the provider and family can adjust dosing or duration as needed. Increasing the dose of gluten further may improve diagnostic yield if tolerated, although in select cases a lower dose and shorter course (6-12 weeks) may be sufficient. There is consensus that mild elevations in celiac serology (<10 times the upper limit of normal) or symptoms, while supportive are not diagnostic for CD. Current North American Society for Pediatric Gastroenterology, Hepatology and Nutrition guidelines recommend histologic findings of intraepithelial lymphocytosis, crypt hyperplasia, and villous atrophy as the accurate and most appropriate endpoint for gluten challenge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.044 |
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".