The Impact of Chronic Pancreatitis Etiology on Clinical Outcomes: A Population-Based Propensity-Matched Analysis
Bibliographic record
Abstract
Background: Chronic pancreatitis (CP) is a complex disease with various underlying etiologies, including alcohol consumption, smoking, autoimmune disorders, genetic predispositions, and other less common causes. Despite extensive research, the impact of these different etiologies on disease progression, complication rates, and long-term outcomes remains insufficiently understood. In particular, the distinction between alcohol-related chronic pancreatitis (ARCP) and non-alcohol-related chronic pancreatitis (NARCP) is not well established in terms of prognosis and therapeutic needs. Methods: We conducted a retrospective cohort study utilizing the TriNetX US Collaborative Network to compare baseline characteristics and clinical outcomes of ARCP versus NARCP. Propensity score matching (PSM) was applied to balance baseline characteristics between both cohorts. Primary outcome was mortality, while secondary outcomes included exocrine pancreatic insufficiency (EPI), pseudocyst formation, development of diabetes and pancreatic cancer, and need for endoscopic retrograde cholangiopancreatography (ERCP) and celiac plexus injection. Results: A total of 203,432 patients with CP were identified, including 11,696 ARCP and 200,560 with NARCP. After PSM (11,678 per group), ARCP was associated with significantly lower rates of mortality (13.0% vs. 16.2%; risk ratio (RR) 0.80), diabetes (22.9% vs. 35.8%; RR 0.64), exocrine pancreatic insufficiency (2.0% vs. 6.1%; RR 0.32), pancreatic cancer (1.1% vs. 8.4%; RR 0.14), and pseudocyst formation (7.1% vs. 9.7%; RR 0.73) compared to NARCP (all P < 0.001). ARCP patients also had lower rates of celiac plexus injection (0.1% vs. 0.8%; RR 0.12) and ERCP (2.3% vs. 10.2%; RR 0.23) (both P < 0.001). Conclusion: In this large, retrospective cohort study, patients with ARCP demonstrated lower rates of mortality, complications, and need for interventions compared to those with NARCP. These findings highlight potential differences in disease progression and clinical management between ARCP and NARCP. Further studies are needed to elucidate underlying mechanisms contributing to these disparities and to refine patient-specific treatment approaches.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".