Post-hepatectomy venous thromboembolism: a systematic review with meta-analysis exploring the role of pharmacological thromboprophylaxis
Bibliographic record
Abstract
Abstract Purpose Patients undergoing hepatectomy are at moderate-to-high risk of venous thromboembolism (VTE). This study critically examines the efficacy of combining pharmacological (PTP) and mechanical thromboprophylaxis (MTP) versus only MTP in reducing VTE events against the risk of hemorrhagic complications. Methods A systematic review of major reference databases was undertaken, and a meta-analysis was performed using common-effects model. Risk of bias assessment was performed using Newcastle–Ottawa scale. Trial sequential analysis (TSA) was used to assess the precision and conclusiveness of the results. Results 8 studies (n = 4238 patients) meeting inclusion criteria were included in the analysis. Use of PTP + MTP was found to be associated with significantly lower VTE rates compared to only MTP (2.5% vs 5.3%; pooled RR 0.50, p = 0.03, I2 = 46%) with minimal type I error. PTP + MTP was not associated with an increased risk of hemorrhagic complications (3.04% vs 1.9%; pooled RR 1.54, p = 0.11, I2 = 0%) and had no significant impact on post-operative length of stay (12.1 vs 10.8 days; pooled MD − 0.66, p = 0.98, I2 = 0%) and mortality (2.9% vs 3.7%; pooled RR 0.73, p = 0.33, I2 = 0%). Conclusion Despite differences in the baseline patient characteristics, extent of hepatectomy, PTP regimens, and heterogeneity in the pooled analysis, the current study supports the use of PTP in post-hepatectomy patients (grade of recommendation: strong) as the combination of PTP + MTP is associated with a significantly lower incidence of VTE (level of evidence, moderate), without an increased risk of post-hepatectomy hemorrhage (level of evidence, low).
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| 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".