Gluten immunogenic peptide excretion detects dietary transgressions in treated celiac disease patients
Bibliographic record
Abstract
BACKGROUND: Life-long removal of gluten from the diet is currently the only way to manage celiac disease (CeD). Until now, no objective test has proven useful to objectively detect ingested gluten in clinical practice. Recently, tests that determine consumption of gluten by assessing excretion of gluten immunogenic peptides (GIP) in stool and urine have been developed. Their utility, in comparison with conventional dietary and analytical follow-up strategies, has not been fully established. AIM: To assess the performance of enzyme-linked immunosorbent assay (ELISA) and point-of-care tests (PoCTs) for GIP excretion in CeD patients on gluten-free diet (GFD). METHODS: We conducted an observational, prospective, cross-sectional study in patients following a GFD for at least two years. Using the Gastrointestinal Symptom Rating Scale questionnaire, patients were classified at enrollment as asymptomatic or symptomatic. Gluten consumption was assessed twice by 3-d dietary recall and GIP excretion (by ELISA in stool and PoCTs (commercial kits for stool and urine) in two consecutive samples. These samples and dietary reports were obtained 10 day apart one from the other. Patients were encouraged to follow their usual GFD during the study period. RESULTS: : 0.317). PoCT detected dietary indiscretions. Both ELISA and PoCT in stool were concordant (concomitantly positive or negative) in 67 out of 74 (90.5%) samples. Excretion of GIP was detected in 7 (8.4%) stool and/or urine samples from patients considered to be strictly compliant with the GFD by dietary reports. CONCLUSION: GIP detects dietary transgressions in patients on long-term GFD, irrespective of the presence of symptoms. PoCT for GIP detection constitutes a simple home-based method for self-assessment of dietary indiscretions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".