Incidence and risk factors of postoperative acute pancreatitis after pancreaticoduodenectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Postoperative acute pancreatitis (POAP) is a specific complication after pancreatectomy. The acute inflammatory response of the residual pancreas may affect the healing of pancreatoenteric anastomoses, leading to postoperative pancreatic fistulas (POPFs), abdominal infections, and even progressive systemic reactions, conditions that negatively affect patients' prognoses and can cause death. However, to the best of our knowledge, no systematic reviews or meta-analytic studies have assessed the incidence and risk factors of POAP after pancreaticoduodenectomy (PD). Method We searched PubMed, Web of Science, Embase, and Cochrane Library databases for relevant literature describing the outcomes of POAP after PD until November 25, 2022, and we used the Newcastle–Ottawa Scale to assess the quality of the studies. Next, we pooled the incidence of POAP and the odds ratios (ORs) and 95% confidence intervals (CIs) of the risk factors using a random-effect meta-analysis. I2 tests were used to assess heterogeneity between the studies. Results We analyzed data from 7,164 patients after PD from 23 articles that met the inclusion criteria for this study. The subgroup results of the meta-analysis by different POAP diagnostic criteria showed that the incidences of POAP were 15% (95% CI, 5–38) in the International Study Group for Pancreatic Surgery group, 51% (95% CI, 42–60) in the Connor group, 7% (95% CI, 2–24) in the Atlanta group, and 5% (95% CI, 2–14) in the unclear group. Being a woman [OR (1.37, 95% CI, 1.06–1.77)] or having a soft pancreatic texture [OR (2.56, 95% CI, 1.70–3.86)] were risk factors of POAP after PD. Conclusion The results showed that POAP was common after PD, and its incidence varied widely according to different definitions. Large-scale reports are still needed, and surgeons should remain aware of this complication. Systematic Review Registration identifier: CRD42022375124.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".