Meta-analysis of randomized trials comparing combined compression and anticoagulation with either modality alone for prevention of venous thromboembolism after surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.044 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it