Celiac Disease and IgA Deficiency: Complications of Serological Testing Approaches Encountered in the Clinic
Bibliographic record
Abstract
BACKGROUND: IgA deficiency causes false-negative IgA-based celiac serology results in patients with celiac disease. Using a case-finding strategy, we examined the prevalence of IgA deficiency, physician evaluation, and management of IgA deficiency during serological testing for celiac disease. METHODS: We reviewed consecutive IgA-endomysial antibody (EMA) and serum IgA results from the laboratory database over 17 months. We cross-referenced seronegative patients with IgA deficiency (IgA <0.06 g/L) to the pathology database to evaluate intestinal biopsy results. Ordering physicians received a questionnaire regarding the management of seronegative patients with IgA deficiency who had no biopsy record. RESULTS: Among the 9533 patients tested for IgA-EMA, 4698 (49%) were tested for IgA deficiency. IgA deficiency occurred in 35 of 4698 (0.75%) patients screened for IgA deficiency. Only 19 of 35 (54%) IgA-deficient patients were diagnosed appropriately with either intestinal biopsy (17 patients) or measurement of IgG-tissue transglutaminase (2 patients). Thirteen (76%) of the 17 IgA-deficient patients who underwent upper endoscopy with or without colonoscopy displayed gastrointestinal pathology on biopsies, including 3 (18%) with celiac disease. No further evaluation to exclude celiac disease was performed for the remaining 16 of 35 (46%) IgA-deficient, EMA-negative patients because of inappropriate management (6 patients), administrative error (7 patients), or patient/physician refusal (3 patients). CONCLUSIONS: IgA deficiency occurred in 1:131 patients tested for celiac disease, and celiac disease occurred in 1:6 of those properly evaluated. Inadequate evaluation of IgA deficiency while testing for celiac disease occurred frequently and resulted in the underdiagnosis of both. Changes in testing algorithms and reporting of results were made to improve testing for celiac disease and IgA deficiency.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".