Efficacy and Safety of Low Molecular Weight Heparin Versus Unfractionated Heparin for Prevention of Venous Thromboembolism in Trauma Patients
Bibliographic record
Abstract
PURPOSE: Trauma patients are at high risk of VTE. We summarize the efficacy and safety of LMWH versus UFH for the prevention of VTE in trauma patients. METHODS: We searched 6 databases from inception through March 12, 2021. We included randomized controlled trials (RCTs) or observational studies comparing LMWH versus UFH for thromboprophylaxis in adult trauma patients. We pooled effect estimates across RCTs and observational studies separately, using random-effects model and inverse variance weighting. We assessed risk of bias using the Cochrane tool for RCTs and the Risk of Bias in Non-Randomized Studies (ROBINS)-I tool for observational studies and assessed certainty of findings using Grading of Recommendations, Assessment, Development and Evaluations methodology. RESULTS: We included 4 RCTs (879 patients) and 8 observational studies (306,747 patients). Based on pooled RCT data, compared to UFH, LMWH reduces deep vein thrombosis (RR 0.67, 95% CI 0.50 to 0.88, moderate certainty) and VTE (RR 0.68, 95% CI 0.51 to 0.90, moderate certainty). As compared to UFH, LMWH may reduce pulmonary embolism [adjusted odds ratio from pooled observational studies 0.56 (95% CI 0.50 to 0.62)] and mortality (adjusted odds ratio from pooled observational studies 0.54, 95% CI 0.45 to 0.65), though based on low certainty evidence. There was an uncertain effect on adverse events (RR from pooled RCTs 0.80, 95% CI 0.48 to 1.33, very low certainty) and heparin induced thrombocytopenia [RR from pooled RCTs 0.26 (95% CI 0.03 to 2.38, very low certainty)]. CONCLUSIONS: Among adult trauma patients, LMWH is superior to UFH for deep vein thrombosis and VTE prevention and may additionally reduce pulmonary embolism and mortality. The impact on adverse events and heparin induced thrombocytopenia is uncertain.
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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 | 0.021 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".