Efficacy and Safety of Anti-Xa-Guided Versus Fixed Dosing of Low Molecular Weight Heparin for Prevention of Venous Thromboembolism in Trauma Patients
Bibliographic record
Abstract
PURPOSE: Trauma patients are at high risk of venous thromboembolism (VTE). We summarize the comparative efficacy and safety of anti-Xa-guided versus fixed dosing for low molecular weight heparin (LMWH) for the prevention of VTE in adult trauma patients. METHODS: We searched Medline and Embase from inception through June 1, 2022. We included randomized controlled trials or observational studies comparing anti-Xa-guided versus fixed dosing of LMWH for thromboprophylaxis in adult trauma patients. We incorporated primary data from 2 large observational cohorts. We pooled effect estimates using a random-effects model. We assessed risk of bias using the ROBINS-I tool for observational studies and assessed certainty of findings using GRADE methodology. RESULTS: We included 15 observational studies involving 10,348 patients. No randomized controlled trials were identified. determined that, compared to fixed LMWH dosing, anti-Xa-guided dosing may reduce deep vein thrombosis [adjusted odds ratio (aOR); 0.52, 95% CI: 0.40-0.69], pulmonary embolism (aOR: 0.48, 95% CI: 0.30-0.78) or any VTE (aOR: 0.54, 95% CI: 0.42-0.69), though all estimates are based on low certainty evidence. There was an uncertain effect on mortality (aOR: 1.06, 95% CI: 0.85-1.32) and bleeding events (aOR: 0.84, 95% CI: 0.50-1.39), limited by serious imprecision. We used several sensitivity and subgroup analyses to confirm the validity of our assumptions. CONCLUSION: Anti-Xa-guided dosing may be more effective than fixed dosing for prevention of deep vein thrombosis, pulmonary embolism, and VTE for adult trauma patients. These promising findings justify the need for a high-quality randomized study with the potential to deliver practice changing results.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".