Discordance Between Serology and Histology for Celiac Disease in a Cohort with Coexisting Liver Disorders
Bibliographic record
Abstract
Abstract Background The liver and celiac disease (CeD) share a complex relationship. While in some patients, isolated hypertransaminasemia is the only manifestation of CeD, liver diseases (LD) may also be associated with the presence of isolated tissue transglutaminase antibodies IgA (tTG IgA) without histologic evidence of CeD. Aims To examine the yield of tTG IgA testing (a) in the workup for chronic liver disease (CLD) or cytolysis and (b) to identify biopsy-confirmed CeD (BxCeD) among patients with concomitant LD. Methods Retrospective study including two cohorts. Cohort 1 represented 444 consecutive individuals without known CeD for which liver specialists requested tTG IgA. Incidence of positive tTG and BxCeD was evaluated. Cohort 2 included 212 consecutive individuals with positive tTG IgA and subsequent duodenal biopsies. The frequency and clinical characteristics of individuals without BxCeD were examined, with and without concurrent LD. Results The rate of first time positive tTG IgA among the tests requested by a liver specialist (cohort 1) was 2.0% (n = 9). However, 33.0% (n = 3) of these patients did not have BxCeD. Cohort 2 included 33 individuals with coexisting LD, of which 42.4% did not have BxCeD, compared with 16.2% of the patients without LD (P < 0.001). The majority of the patients without BxCeD (65.1%) showed an increase < 3 times upper limit of normal of tTG IgA. Conclusions Although there is clinical value in testing for CeD in the context of LD, there could be a high rate of positive CeD serology unaccompanied by histologic signs in patients with coexisting LD.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".