HbA1c and the Severity of Acute Pancreatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: HbA1c, a measure of long-term glycaemic control, has been identified as a potential prognostic risk factor for pancreatitis severity, yet there is a paucity of evidence on its association with pancreatitis outcomes in people with and without diabetes. We, therefore, conducted a systematic review and meta-analysis to assess the current body of evidence. METHODS: Articles from January 1980 to March 2025 were screened using PubMed and the Excerpta Medica database (Embase). Randomised control trials (RCTs), cohort, and case-control studies were permitted for inclusion if they used an appropriate method for both acute pancreatitis (AP) diagnosis and severity classification. Quality assessment was performed using the Newcastle-Ottawa scale (NOS), and random effects models reporting pooled odds ratios (ORs) were estimated in our meta-analyses. RESULTS: Our search generated 2,270 results, from which two studies were eligible for inclusion with a total of 1,195 participants. Both studies were deemed to be at low risk of bias. The results of our meta-analyses demonstrated an increased odds of developing severe AP with increased HbA1c levels (OR = 2.14, 95% confidence interval (CI) 1.32-3.48). Elevated HbA1c levels were also found to increase the odds for developing local pancreatic complications (OR = 1.71, 95% CI 1.25-2.34) and systemic complications (pooled OR = 2.82, 95% CI 0.49-16.28). CONCLUSIONS: Our review suggests that elevated HbA1c levels may increase the likelihood of developing severe AP as well as local and systemic complications. The results of the review are limited due to the small number of included studies. We recommend that large multicentre cohort studies be conducted to further investigate this relationship.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".