Diagnostic and therapeutic strategies for biliary strictures: a comparison of ACG, ESGE, and ASGE guidelines
Bibliographic record
Abstract
INTRODUCTION: The diagnostic management of biliary strictures remains a complex clinical challenge requiring evidence-based guidance. Multiple societies, including the European Society of Gastrointestinal Endoscopy (ESGE), American College of Gastroenterology (ACG), and American Society for Gastrointestinal Endoscopy (ASGE), have recently published guidelines with both consensus and divergence. AREAS COVERED: A comparative analysis of the ESGE, ACG, and ASGE guidelines reveals shared principles, such as the role of MRI/MRCP in initial evaluation (moderate quality evidence), limited utility of tumor markers alone (low to very low quality evidence), and preference for metal stents in palliation (moderate quality evidence). Key differences arise in tissue acquisition for perihilar strictures, with ACG/ASGE discouraging EUS-guided sampling in surgical candidates (low-quality evidence), while ESGE conditionally supports its use in non-resectable cases candidates (low-quality evidence). The ASGE provides conditional recommendations on bilateral stenting for malignant hilar obstruction, not emphasized in other guidelines. EXPERT OPINION: Although founded on similar evidence, guideline variations reflect differing risk tolerance and resource considerations. Clinicians must recognize these nuances to tailor management. Future research should clarify EUS seeding risk, evaluate cost-effectiveness, and inform harmonized guideline updates.
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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 | 0.022 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".