Evidence-Informed Expert Recommendations for the Management of Celiac Disease in Children
Bibliographic record
Abstract
Although the need for effective long-term follow-up for patients with celiac disease (CD) has been recognized by many expert groups, published practice guidelines have not provided a clear approach for the optimal management of these patients. In an attempt to provide a thoughtful and practical approach for managing these patients, a group of experts in pediatric CD performed a critical review of the available literature in 6 categories associated with CD to develop a set of best practices by using evidence-based data and expert opinion. The 6 categories included the following: bone health, hematologic issues, endocrine problems, liver disease, nutritional issues, and testing. Evidence was assessed by using standardized criteria for evaluating the quality of the data, grade of evidence, and strength of conclusions. Over 600 publications were reviewed, and 172 were chosen for inclusion. The thorough review of the results demonstrated that the quality of the data available was often insufficient to provide unequivocal best practices. However, using the available data and the clinical experience of the panel, a practical framework for the management of children with CD was created. These recommendations were developed by our expert panel and do not necessarily reflect the policy of the American Academy of Pediatrics. The potential usefulness of these best practices is underscored by the fact that consensus, measured by the outcome of anonymous voting, was reached by the panel for 24 of the 25 questions. We hope that these best practices may be useful to the pediatric gastroenterology and larger general pediatric communities.
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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 | 0.054 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".