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Screening for Celiac Disease in Children With Recurrent Abdominal Pain

2001· article· en· W2093541487 on OpenAlexaff
Kelly P. Fitzpatrick, Philip M. Sherman, Moshe Ipp, Norman R. Saunders, Colin Macarthur

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2001
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenSickKids Foundation
Fundersnot available
KeywordsMedicineAbdominal painConfidence intervalInternal medicineDiseasePediatrics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.270
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2001
Admission routes1
Has abstractyes

Explore more

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