Screening for Celiac Disease in Children With Recurrent Abdominal Pain
Bibliographic record
Abstract
BACKGROUND: The clinical presentation of celiac disease--a life-long gluten intolerance--may be characterized by chronic abdominal pain. The objective of this study was to determine if children with recurrent abdominal pain had a higher prevalence of antiendomysial antibodies (a serologic marker of celiac disease) compared with healthy children. METHODS: Children with recurrent abdominal pain and healthy control participants were recruited from the offices of community pediatricians. Serum samples were drawn and antiendomysial antibodies were measured in both groups. Demographic data included age, gender, height, and weight. RESULTS: A total of 200 children were recruited, of whom 173 (87%) had serum samples drawn. Of these, 92 were children with recurrent abdominal pain and 81 were control participants. Only 2 of the 173 samples (1.2%) were positive for antiendomysial antibody. The frequency of antiendomysial antibody positivity in children with recurrent abdominal pain was 1 in 92 (1%; 95% confidence interval, 0-6%) compared with 1 in 81 (1%; 95% confidence interval, 0-7%) in control participants. CONCLUSIONS: This community-based case-control study found no association between recurrent abdominal pain and the prevalence of antiendomysial antibody. Therefore, these data do not support screening for celiac disease in the child with classic recurrent abdominal pain in the primary care setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".