Meta-analysis of randomized trials comparing combined compression and anticoagulation with either modality alone for prevention of venous thromboembolism after surgery
Bibliographic record
Abstract
BACKGROUND: Although venous thromboembolism (VTE) is an important cause of postoperative morbidity and mortality, there is still no consensus on the optimal strategy for VTE prevention after major surgery. The objective of this review was to determine the benefits and risks of thromboprophylaxis with both compression and anticoagulation, compared with either modality alone. METHODS: A systematic review of MEDLINE, CENTRAL and Embase databases was performed to identify eligible randomized trials. The literature search and data extraction were carried out independently by two reviewers. Outcomes of interest were deep vein thrombosis (DVT), pulmonary embolism, bleeding, limb injury and mortality. RESULTS: Twenty-five studies were eligible for inclusion. Adding compression to anticoagulation decreased the risk of DVT by 49 per cent (risk ratio (RR) 0·51, 95 per cent confidence interval 0·36 to 0·73). The corresponding funnel plot suggested publication bias and, overall, the evidence for this comparison was judged to be of low quality. Adding anticoagulation to compression decreased the risk of DVT by 44 per cent (RR 0·56, 0·45 to 0·69) while increasing the risk of bleeding (RR 1·74, 1·29 to 2·34). There was no suggestion of publication bias and the evidence for this comparison was judged to be of moderate quality. CONCLUSION: Combined compression and anticoagulation is more effective at preventing postoperative DVT than either modality alone. However, adding anticoagulation to compression increases the risk of bleeding, and the evidence that adding compression to anticoagulation reduces VTE risk is of low quality.
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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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.059 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".