Global Incidence of Acute Pancreatitis Is Increasing Over Time: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: Acute pancreatitis is a common disease with significant associated morbidity and mortality. We performed a systematic review and meta-analysis of population-based studies to explore the changing temporal trends of acute pancreatitis incidence globally. METHODS: We performed a systematic literature search to identify population-based studies reporting the annual incidence of acute pancreatitis. Abstracts were assessed independently to identify applicable articles for full-text review and data extraction. Joinpoint temporal trend analyses were performed to calculate the average annual percent change (AAPC) with 95% confidence intervals (CIs). The AAPCs were pooled in a meta-analysis to capture the overall and regional trends in acute pancreatitis incidence over time. Temporal data were summarized in a static map and an interactive, web-based map. RESULTS: Forty-four studies reported the temporal incidence of acute pancreatitis (online interactive map: https://kaplan-acute-pancreatitis-ucalgary.hub.arcgis.com/). The incidence of acute pancreatitis has increased from 1961 to 2016 (AAPC, 3.07%; 95% CI, 2.30% to 3.84%; n = 34). Increasing incidence was observed in North America (AAPC, 3.67%; 95% CI, 2.76% to 4.57%; n = 4) and Europe (AAPC, 2.77%; 95% CI, 1.91% to 3.63%; n = 23). The incidence of acute pancreatitis was stable in Asia (AAPC, -0.28%; 95% CI, -5.03% to 4.47%; n = 4). CONCLUSIONS: This meta-analysis provides a comprehensive overview of the global incidence of acute pancreatitis over the last 56 years and demonstrates a steadily rising incidence over time in most countries of the Western world. More studies are needed to better define the changing incidence of acute pancreatitis in Asia, Africa, and Latin America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".