S288 Cancer-Aging Interplay via Senescence Hallmarks: Insights to Gastrointestinal Tumor Mechanisms
Bibliographic record
Abstract
Introduction: Idiopathic acute pancreatitis (IAP) accounts for up to 20% of cases despite comprehensive initial evaluation. Determining the etiology is crucial to guide management and prevent progression to chronic pancreatitis (CP). Endoscopic ultrasound (EUS) and magnetic resonance cholangiopancreatography (MRCP) are second-line imaging modalities, although their relative diagnostic performance remains unclear. This systematic review and meta-analysis compares the diagnostic yield of EUS and MRCP in identifying underlying causes of IAP. Methods: A systematic search of PubMed, EMBASE, and Google Scholar (January 2000–April 2025) identified English-language studies comparing EUS and MRCP diagnostic performance in IAP. Two reviewers independently extracted data and assessed study quality using the Newcastle-Ottawa Scale. Pooled relative risks (RR) were calculated using a random-effects model, with subgroup, sensitivity, and publication bias analyses done per PRISMA 2020. Subgroup analyses assessed diagnostic yield by etiology—biliary disease (cholelithiasis, choledocholithiasis, microlithiasis, and sludge), pancreatic divisum, and malignancy (pancreatic adenocarcinoma and periampullary cancer). Results: Of 4,933 studies screened, 8 met inclusion criteria. Pooled analysis showed EUS had significantly higher diagnostic yield than MRCP (RR = 2.01; 95% CI: 1.42–2.85; P < 0.01; I² = 69.1%). EUS was markedly superior for biliary etiologies (RR = 3.67; 95% CI: 2.08–6.47; P < 0.0001; I² = 37.7%) and trended toward better detection of CP in IAP (RR = 2.20; 95% CI: 0.87–5.55; P = 0.0955; I² = 15.5%). MRCP favored detection of pancreatic divisum (RR = 0.59; 95% CI: 0.31–1.12; P = 0.1078; I² = 0.0%). EUS also demonstrated higher cancer detection compared to MRCP (RR = 1.98; 95% CI: 0.56–7.03; P = 0.2896; I² = 0.0%). Conclusion: EUS surpasses MRCP in diagnosing IAP biliary etiologies and had an overall higher diagnostic yield. Prior studies suggest that EUS may be oversensitive in diagnosing CP, which may be consistent with the increased rate of diagnosis by EUS in our data. There was some signal that EUS had a higher diagnostic yield for cancer which may highlight a potential role in identifying occult malignancy in IAP evaluation. In practice, choosing EUS vs MRCP depends on resource availability, but a patient-centered approach that integrates modality strengths with clinical profiles can improve diagnostic accuracy and prognostic outcomes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".