<i>Helicobacter</i> DNA in bile: correlation with hepato‐biliary diseases
Bibliographic record
Abstract
BACKGROUND: Helicobacter has been identified in isolated cases of hepato-biliary diseases, but its role in the pathogenesis of these conditions remains unclear. AIM: To determine whether Helicobacter could be detected in bile obtained at endoscopic retrograde cholangiopancreatography, and to evaluate the prevalence of this infection in patients with hepato-biliary diseases. METHODS: Bile was collected from 125 patients with various hepato-biliary diseases undergoing endoscopic retrograde cholangiopancreatography. Among them, 75 were diagnosed with biliary stones, 15 with pancreatico-biliary malignancies and four with primary sclerosing cholangitis. The detection of Helicobacter in DNA extracted from these bile samples was performed using Helicobacter genus-specific primers (capable of detecting 100-1000 organisms/mL). RESULTS: Helicobacter was detected in all positive controls. Only three samples had polymerase chain reaction inhibitors. All remaining bile samples (122 patients with hepato-biliary diseases) were negative for Helicobacter DNA. CONCLUSIONS: Helicobacter can be detected in bile samples using polymerase chain reaction. This infection, however, was not present in any of our patients diagnosed with gallstones or hepato-biliary malignancies, raising doubt as to the possible association between Helicobacter and these entities. Given the low sample size of patients with primary sclerosing cholangitis, more studies are required to determine whether an association exists with this condition.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".