Endoscopic ultrasound diagnostic gain over computed tomography and magnetic resonance cholangiopancreatography in defining etiology of idiopathic acute pancreatitis.
Bibliographic record
Abstract
BACKGROUND: About 10%-30% of acute pancreatitis remain idiopathic (IAP) even after clinical and imaging tests, including abdominal ultrasound (US), contrast-enhanced computed tomography (CECT) and magnetic resonance cholangiopancreatography (MRCP). This is a relevant issue, as up to 20% of patients with IAP have recurrent episodes and 26% of them develop chronic pancreatitis. Few data are available on the role of EUS in clarifying the etiology of IAP after failure of one or more cross-sectional techniques. AIM: To evaluate the diagnostic gain after failure of one or more previous cross-sectional exams. METHODS: We retrospectively collected data about consecutive patients with AP and at least one negative test between US, CECT and MRCP, who underwent linear EUS between January 2017 and December 2020. We investigated the EUS diagnostic yield and the EUS diagnostic gain over different combinations of these cross-sectional imaging techniques for the etiologic diagnosis of AP. Types and frequency of EUS diagnosis were also analyzed, and EUS diagnosis was compared with the clinical parameters. After EUS, patients were followed-up for a median of 31.5 mo to detect cases of pancreatitis recurrence. RESULTS: 0.001). During the follow-up, AP recurred in 3 patients, one of which remained idiopathic. CONCLUSION: EUS is a good test to define AP etiology. It showed a 63% diagnostic gain over CECT + MRCP. In suitable patients, EUS should always be performed in cases of IAP. Further prospective studies are needed.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".