ONLY HIGH TITRE ANTI‐TISSUE TRANSGLUTAMINASE ANTIBODIES CONSISTENTLY PREDICT CELIAC DISEASE
Bibliographic record
Abstract
Celiac Disease has been found to be far more common than previously recognized due to the widespread availability of highly sensitive and specific screening tests such as the anti-tissue transglutaminase antibody (TTG). The TTG has been reported to have a greater than 90% specificity and sensitivity. These reports did not look at antibody titres as part of their protocols. This prospective cohort study was designed to assess whether or not a high titre TTG was predictive of celiac disease and how it compared to mid-range titres and the overall predictive value using the recommended screening level titre cut-offs (≥20 U for this commercial kit). Methods: From January 2005 to April 2006, 289 patients age 9 months to 17 years were recruited. 101 potential subjects and 188 controls. All TTG results were either reported from or confirmed at our hospital laboratory. Results: All 188 controls had negative TTG results and biopsy results not in keeping with celiac disease based on the modified Marsh criteria. Of the 101 potential subjects, 3 had low IgA levels (all 3 normal biopsies, not in tables below), 10 with high TTG's on outside labs were normal on repeat testing, and 88 had raised TTG's. See Tables below. Conclusion: Only high titre TTG results (>200 U using our commercial kit) were reliable in predicting positive biopsy results. The sensitivity at the screening test cut-off titres (>20 U) was 100% but the specificity was only 85% (198/234) which is lower than previously reported.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".