A meta-analysis of bridging anticoagulation between low molecular weight heparin and heparin
Bibliographic record
Abstract
BACKGROUND: Patients with mechanical heart valves (MHV) have an increased risk of thromboembolic complications. Low molecular weight heparin (LMWH) and unfractionated heparin (UFH) are often recommended for bridging anticoagulation; however, it is not clear which strategy is more beneficial. METHODS: The PubMed, EMBASE, and Cochrane databases were searched from January 1960 to March 2019. Randomized controlled trials and observational studies were analyzed. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the studies. Stata 11.0 was used for the meta-analysis. RESULTS: A total of 6 publications were included; 1366 events were selected, involving 852 events with LMWH and 514 events with UFH. The thromboembolism risk of the LMWH group was lower than that of the UFH group (risk ratio [RR] = 0.34, 95% confidence interval [CI] 0.12-0.95, P = .039). The incidence of major bleeding was lower in the LMWH group than in the UFH group, albeit without statistical significance (RR = 0.94, 95% CI 0.68-1.30, P = .728), as was mortality (RR = 0.52, 95% CI 0.16-1.66, P = .271). Subgroup analysis showed that LMWH cardiac surgery patients had a higher risk of major bleeding compared with UFH cardiac surgery patients (RR = 1.17, 95% CI 0.72-1.90, P = .526); but among non-cardiac surgery patients, the LMWH group had a lower risk of major bleeding than the UFH group (RR = 0.79, 95% CI 0.51-1.22, P = .284), although the difference was not statistically significant. CONCLUSION: Our meta-analysis suggests that LMWH not only reduces the risk of thromboembolism in patients with MHV but also does not increase the risk of major bleeding. LMWH may provide safer and more effective bridging anticoagulation than UFH in patients with MHV. It is still necessary to conduct future randomized studies to verify this conclusion.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.057 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".