Comparison of Endoscopic Ultrasonography and Magnetic Resonance Cholangiopancreatography in the Diagnosis of Pancreatobiliary Diseases: A Prospective Study
Bibliographic record
Abstract
OBJECTIVES: To compare the diagnostic value of endoscopic ultrasonography (EUS) and magnetic resonance cholangiopancreatography (MRCP) in: (a) patients with a dilated biliary tree unexplained by ultrasonography (US) (group 1), and (b) the diagnosis of choledocholithiasis in patients with nondilated biliary tree (group 2). METHODS: Patients were prospectively evaluated with EUS and MRCP. The gold standard used was surgery or EUS-FNA and ERCP, intraoperative cholangiography, or follow-up when EUS and/or MRCP disclosed or precluded malignancy, respectively. Likelihood ratios (LR) and pretest and post-test probabilities for the diagnosis of malignancy and choledocholithiasis were calculated. RESULTS: A total of 159 patients met one of the inclusion criteria but 24 of them were excluded for different reasons. Thus, 135 patients constitute the study population. The most frequent diagnosis was choledocholithiasis (49% in group 1 and 42% in group 2, P= 0.380) and malignancy was more frequent in group 1 (35%vs 7%, respectively, P < 0.001). When EUS and MRCP diagnosed malignancy, its prevalence in our series (35%) increased up to 98% and 96%, respectively, whereas it decreased to 0% and 2.6% when EUS and MRCP precluded this diagnosis. In patients in group 2, when EUS and MRCP made a positive diagnosis of choledocholithiasis, its prevalence (42%) increased up to 78% and 92%, respectively, whereas it decreased to 6% and 9% when any pathologic finding was ruled out. CONCLUSIONS: EUS and MRCP are extremely useful in diagnosing or excluding malignancy and choledocholithiasis in patients with dilated and nondilated biliary tree. Therefore, they are critical in the approach to the management of these patients.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".