S948 Number of Biopsies Taken for Celiac Disease Is Associated With Specialty and Centre: A Quality Review
Bibliographic record
Abstract
Introduction: Duodenal mucosal biopsy is the gold standard for the clinicopathological diagnosis of celiac disease. Given that the disease can be patchy, multiple endoscopic biopsy samples (minimum 2 from duodenal cap and 4 from mid to distal portion) are recommended for histopathology assessment to support or exclude the diagnosis. The aim was to assess the number of endoscopic duodenal biopsies obtained from patients with a clinical suspicion for celiac disease and explore factors associated with postulated biopsy number variability. Methods: Retrospective review of all duodenal biopsy cases obtained in 2023 from adult patients (18 years of age and older) undergoing upper gastrointestinal (GI) endoscopy performed at 2 academic and 3 community hospitals by gastroenterologists and surgeons. Pathology assessment of all cases was completed at one academic hospital pathology laboratory and reported by one of several GI subspecialty pathologists. Individual pathology reports were reviewed for patient age, endoscopist (including specialist type), procedure location, clinical indication for endoscopic examination, duodenal biopsy number and biopsy location (duodenal cap; mid to distal portion), total duodenal biopsy number, and pathology diagnosis. Cases with neoplastic disease were also excluded from analysis. Statistical analysis included descriptive statistics and comparison of means through student t test and ANOVA. Results: One thousand one hundred and sixty patients had duodenal biopsies performed during the study period. The mean age was 58.7 (17.7) years. Thirty-four individual endoscopists from 5 hospitals were part of the cohort; 48.4% of samples were submitted by surgeons. When all duodenal biopsies were considered, the mean number of biopsies taken were 3.42. Gastroenterologists submitted a higher mean number of biopsies than surgeons - 4.14 (1.35) versus 2.65 (1.35) (P< 0.001). Differences were also seen when hospitals were compared, with one hospital having the highest mean number at 4.11 (1.4) ranging down to the lowest at 2.5 (1.57) (P< 0.001). When only duodenal biopsies submitted with the question “rule out celiac disease” were considered (n=833 patients), results were similar. Conclusion: This study shows that duodenal biopsy patterns are not meeting standards, even when specifically assessing for celiac disease, with differences seen between centres and specialties. Next steps will include targeted interventions to attempt to correct.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".