Positive Predictive Value of Tissue Transglutaminase IgA for Celiac Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Recent changes in European diagnostic criteria allow for serologic diagnosis of celiac disease in children. Those guidelines have not been adopted in North America; hence, we aim to assess the positive predictive value (PPV) of tissue transglutaminase (tTG) immunoglobulin A (IgA) assays used in North America in identifying histologic findings of celiac disease. METHODS: Multicenter retrospective cohort study of children (<18 years) with an elevated tTG IgA within 6 months of an esophagogastroduodenoscopy between January 2016 and December 2021. Biopsy-confirmed celiac disease was determined by the presence of intraepithelial lymphocytosis and villous atrophy. The primary outcomes were the PPV of an elevated tTG IgA and tTG IgA greater than or equal to 10 times the upper limit of normal (10× ULN). RESULTS: Overall, 4019 children (63.3% female; 9% type 1 diabetes, 2% Down syndrome) were included. Histologic findings were consistent with celiac disease for 3321 children (PPV = 82.6% [95% CI, 81.4-83.8]). Among the 1739/4019 (43.2%) children with tTG IgA greater than or equal to 10× ULN, 1651 had biopsy-confirmed celiac disease (PPV10× = 94.9% [95% CI, 93.8-95.9]). Five percent (88/1739) of children did not have histologic findings of celiac disease, including 41/1739 (2%) with normal histology. Diagnostic accuracy of tTG IgA varied widely among assays used in North America (PPV range: 71.5%-88.8%; PPV10× range: 89.3%-97.3%). Assays performed worse in children with type 1 diabetes (PPV10× 89% [95% CI, 83.5-92.8]). CONCLUSIONS: Elevated tTG IgA in isolation is insufficient to confidently diagnose celiac disease. As tTG assay performance varied widely, diagnostic confirmation by a specialist prior to dietary changes is essential.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".