Can Tissue Transglutaminase Antibody Titers Replace Small-Bowel Biopsy to Diagnose Celiac Disease in Select Pediatric Populations?
Bibliographic record
Abstract
OBJECTIVES: The use of screening tests for celiac disease has increased the number of patients referred for evaluation. We proposed that the subgroup of patients with very high tissue transglutaminase antibody (TTG) titers is positive for celiac disease and a small-bowel biopsy is not necessary to make the diagnosis. A gluten-free diet should be attempted and, if the patient's symptoms do not improve, then a biopsy should be performed to confirm the diagnosis. METHODS: A chart review of data for 103 patients who underwent both TTG testing and a small-bowel biopsy was performed. We examined the impact of using TTG values of >100 U and <20 U as cutoff values and suggested performing biopsies for patients with TTG values of 20 to 100 U, as is current practice. RESULTS: Fifty-eight of 103 patients demonstrated positive biopsy results. Forty-nine of 103 patients had TTG levels of >100 U, with 48 of 49 exhibiting positive biopsy results. Only 7 of 16 patients with TTG values of 20 to 100 U exhibited positive biopsy results. Three patients with TTG levels of <20 U had positive biopsies; 2 were IgA negative and 1 had a duodenal ulcer. With the cutoff values of >100 U and <20 U with known IgA status, the sensitivity was 0.980 (48 of 49 cases) and the specificity was 0.972 (35 of 36 cases). An incremental cost analysis found that this proposal could potentially decrease the costs of investigation and diagnosis by almost 30%. CONCLUSIONS: When the cutoff values were changed to >100 and <20 U and IgA levels were verified, the sensitivity and specificity were very high. Patients with mid-range TTG values (20-100 U) or values of <20 U with negative IgA status should continue to undergo biopsies for diagnosis of celiac disease.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".