Comparison of IgA Endomysium Antibody and IgA Tissue Transglutaminase Antibody in Celiac Disease
Bibliographic record
Abstract
The antigen for immunoglobulin (Ig) A endomysium antibody (EmA), a sensitive and specific serological marker for celiac disease, has recently been described as tissue transglutaminase (tTG). The aim of this study was to compare the assays used to measure IgA EmA and IgA tTG antibody in patients with celiac disease and disease control subjects. Sera from 21 patients with untreated celiac disease, 48 patients with treated celiac disease and 128 disease control subjects were tested both for IgA EmA with the use of indirect immunofluorescence against human umbilical cord and for IgA tTG antibody with the use of ELISA. Titres of IgA tTG antibody were significantly higher in both the untreated and treated celiac groups than in the disease control group. Titres in the treated group were, however, significantly lower than in the untreated group. A reference range was calculated to include 99.8% of the disease control group in whom small bowel biopsy showed no evidence of celiac disease. One patient from the disease control group with raised IgA tTG antibody titres and positive IgA EmA was found to have celiac disease on small bowel biopsy. The sensitivity, specificity, and positive and negative predictive values of the IgA EmA assay were all 100%. The sensitivity of the IgA tTG antibody assay was 95%, specificity 100%, positive predictive value 100% and negative predictive value 97.7%. An ELISA used to measure IgA tTG antibody is an excellent tool to screen for celiac disease and may prove useful for monitoring response to treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".