A North American Serologic‐based Celiac Disease Diagnosis
Bibliographic record
Abstract
To the Editor: In the January issue, Husby et al (1) published new ESPGHAN guidelines (last updated in 2012) for Celiac enteropathy diagnosis. The new clinical approach still uses immunoglobulin A antibodies against tissue transglutaminase (TTG-IgA) greater than 10 times the upper limit of normal as sufficient for diagnosis, with a welcome change that includes removing the suggestion of obtaining HLA genotyping to confirm their diagnosis. This decision was based on both prospective and retrospective studies, which all replicated and confirmed the 2012 guidelines and showed that the high serologic tests have >98% positive-predictive value (PPV) of enteropathy. The Europeans have now updated their celiac disease diagnostic guidelines twice, within 1 decade. The currently posted NASPGHAN guidelines were last updated in 2005 (2). Since that time, several North American patient cohort studies have provided evidence supporting the practice of serologically based, nonbiopsy diagnosis. Our 2016 large cohort study (3) had over 500 biopsy-proven subjects and showed similar PPV and false-positive results of TTG-IgA to the ESPGHAN studies also providing a shared decision-making model for gastroenterologists. Another study from Canada supports ESPGHAN's guidelines and also demonstrated no difference in gluten-free diet adherence in those diagnosed serologically versus those with biopsies (4). Especially now, in the current model of medicine with overpriced medical costs, poor insurance support for deductibles, and value-driven care incentives, NASPGHAN members should strongly consider adopting the ESPGHAN guidelines for nonbiopsy diagnosis. This approach provides a shared decision-making model for families, cost reduction, and a more unified, consistent approach for this relatively common autoimmune disorder.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".