Incidence and treatment of splanchnic vein thrombosis in patients with acute pancreatitis: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: This meta-analysis aimed to estimate the incidence of splanchnic vein thrombosis (SVT) in patients with acute pancreatitis and assess the effects of therapeutic anticoagulation. METHODS: Systematic searches of the Medline, Embase, and Cochrane databases were undertaken to identify studies reporting the incidence and outcomes associated with SVT in patients with acute pancreatitis. The pooled incidence, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a random effects model. PROSPERO database registration no. CRD 42021230912. RESULTS: Only 18 of the 238 studies identified met the inclusion criteria. Of the 943 patients who had SVT, 264 (28.0%) received anticoagulation. The pooled incidence of SVT at first presentation of acute pancreatitis was 15% (95% CI 5 to 26%), but was 17% (95% CI 14 to 20%) in all studies. Recanalization was more likely to occur in the anticoagulation-treated than in the untreated group (OR 0.51, 95% CI 0.31 to 0.83, P = 0.007). There were no differences in hemorrhagic complications (OR 2.27, 95% CI 0.81 to 6.37, P = 0.12) or overall mortality (OR 2.37, 95% CI 0.86 to 6.52, P = 0.10) in relation to the use of anticoagulation. The overall incidence of portal hypertension in patients was 60% (95% CI 55 to 65%). However, it was not possible to determine the incidence in each group. CONCLUSIONS: The incidence of SVT in patients with acute pancreatitis is significant. Treatment with anticoagulants improved the odds of recanalization but did not increase the risk of hemorrhagic complications or overall mortality.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".