Comparing fondaparinux and low molecular weight heparin for thromboprophylaxis after hip and knee arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
Venous thromboembolism (VTE) remains a significant cause of perioperative morbidity and mortality despite the availability of prophylactic medications. There has been a debate about which thromboprophylaxis medication, Fondaparinux or low-molecular weight heparin (LMWH), is better after hip and knee arthroplasty. We have compared these two treatment regimens in our study. Electronic databases like PubMed, Cochrane, and ScienceDirect were searched from inception to August 2024. The weighted mean difference (WMD) for continuous outcomes and risk ratio (RR) for dichotomous outcomes were pooled using the Review Manager software version 5.4.1, and a random effects model was employed. The Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool (ROB 2.0) were used to assess the quality of the included studies. Publication bias was evaluated visually through funnel plots and statistically through Egger's regression. GRADE assessment was used to analyze the certainty of evidence. A total of 17 studies, 9 Cohorts, and 8 Randomized controlled trials (RCTs) pooling a total of 74 499 patients were included in this meta-analysis. Fondaparinux showed a statistically significant reduction in the risk of VTE [0.59; 95% confidence interval (CI): [0.48, 0.71]; P < 0.00001; I2 = 36%] and deep venous thrombosis (DVT) (RR = 0.75, 95% CI: [0.56, 1.00]; P = 0.05; I2 = 68%) compared to LMWH. Major bleeding (RR = 2.06, 95% CI: [1.19, 3.57]; P = 0.01; I2 = 43%), surgical site bleeding (RR = 1.67, 95% CI: [1.04, 2.66]; P = 0.03; I2 = 9%), and postoperative transfusions (RR = 1.07, 95% CI: [1.02, 1.12]; P = 0.004; I2 = 0%) were significantly higher in the Fondaparinux group. Symptomatic VTE, pulmonary embolism, mortality, and operating time showed no significant difference between the two groups. In conclusion, Fondaparinux is superior to LMWH in VTE and DVT prophylaxis. However, it is associated with an increased risk of major bleeding, surgical site bleeding, and postoperative transfusions.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".