Gastric Biopsies: The Gap between Evidence-Based Medicine and Daily Practice in the Management of Gastric<i>Helicobacter pylori</i>Infection
Bibliographic record
Abstract
BACKGROUND: Many consider histology to be the gold standard for Helicobacter pylori detection. Because the number and distribution of H pylori organisms vary, particularly in patients taking proton pump inhibitors (PPIs), the American Gastroenterological Association recommends discontinuing PPIs two weeks before endoscopy, and taking biopsies from both the body and antrum. OBJECTIVE: To assess the influence of clinical practice on the histopathological detection of H pylori infection. METHODS: Electronic patient records were evaluated for the sites of gastric sampling and PPI use at endoscopy. One hundred fifty cases with biopsies taken from both antrum and body were randomly selected for pathological re-review with special stains. The gastric regions sampled, H pylori distribution and influence of clinical factors on pathological interpretation were assessed. RESULTS: Between 2005 and 2010, 10,268 biopsies were taken to detect H pylori. Only one region was sampled in 60% of patients (antrum 47%, body 13%). Re-review of biopsies taken from both antrum and body indicated that the correct regions were sampled in only 85 (57%) patients. Of these, 54 were H pylori positive and 96 were H pylori negative. H pylori was present in the antrum in only 15% of the patients and body only in 21%. Of 96 H pylori-negative patients, two were reinterpreted as positive. Forty-seven per cent of patients were taking PPIs at endoscopy, contributing to both false-negative and false-positive diagnoses. CONCLUSION: Despite national and international guidelines for managing H pylori infection, the American Gastroenterological Association guidelines are infrequently adhered to, with PPIs frequently contributing to false diagnosis; sampling one region only increases the likelihood of missing active infection by at least 15%.
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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 | 0.056 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".