The prevalence and risk factors for portal vein thrombosis following hepatectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The prevalence and risk factors of portal vein thrombosis (PVT) are largely unclear, with an increasing number of studies reporting inconsistent results. AIM: The current study aimed to evaluate the prevalence and risk factors of PVT following hepatectomy through a systematic review and meta‑analysis. MATERIALS AND METHODS: A comprehensive literature search was conducted across multiple databases (PubMed, Embase, and the Cochrane Library) to identify relevant studies. Prospective and retrospective studies reporting on PVT following hepatectomy were included. The Newcastle‑Ottawa Scale (NOS) was used to assess study quality, and the random effects model was used to analyze the prevalence and risk factors. Result: A total of 15 studies involving 5145 patients were included in the current meta‑analysis. The pooled prevalence of PVT following hepatectomy was 9% (95% CI, 7%-12%) with substantial heterogeneity (I2 = 93.1%). Subgroup analyses showed that a prospective design and larger sample size were associated with lower prevalence rates. PVT prevalence was higher among the patients undergoing simultaneous splenectomy and hepatectomy. Liver cirrhosis (odds ratio [OR], 5.18; 95% CI, 1.85-14.47), portal vein resection (OR, 5.07; 95% CI, 2.2-11.66), and right‑sided hepatectomy (OR, 6.26; 95% CI, 1.8-21.76) were significant risk factors for PVT. CONCLUSIONS: PVT is a notable complication following hepatectomy, with an overall prevalence of 9%. Specific factors that significantly increase the risk of PVT include liver cirrhosis, portal vein resection, and right‑sided hepatectomy.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".