Celiac disease in North America: What is the current practice of pediatric gastroenterology providers?
Bibliographic record
Abstract
Objectives: While guidelines exist for the diagnosis and management of pediatric celiac disease (CeD), current practices in North America are not well-described. This study aimed to explore current practice patterns to identify gaps and direct future clinical, training and research initiatives. Methods: A 23-item survey designed by the Celiac Disease Special Interest Group was distributed electronically to its members. Questions explored four themes: (1) screening and diagnosis pre and post the coronavirus disease (COVID)-19 pandemic, (2) treatment and monitoring, (3) family screening and transition of care, and (4) CeD focused training. Results: = 24). While endoscopy remained the gold standard, serology-based diagnosis was accepted by 47.5% (132/278). In response to the COVID-19 pandemic, 37.4% of providers changed their diagnostic practice. Barriers to care included: lack of insurance coverage for dietitians, wait times, and lack of CeD focused training. During fellowship 69.1% (192/278) reported no focused CeD training. Conclusion: Survey results revealed practice variation regarding the diagnosis and management of CeD in North America including a substantial proportion accepting non-biopsy, serology-based diagnosis, which increased during the COVID-19 pandemic. Variations in screening, diagnosis, interval surveillance, and family screening were also identified. Dedicated CeD education in pediatric gastroenterology fellowship may be an opportunity for standardizing practice and advancing research. Future North American guidelines should take current care patterns into consideration and develop new initiatives to improve care of children with CeD.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".