Pharmacological thromboprophylaxis to prevent venous thromboembolism in patients with temporary lower limb immobilization after injury: systematic review and network meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Thromboprophylaxis has the potential to reduce venous thromboembolism (VTE) following lower limb immobilization resulting from injury. OBJECTIVES: We aimed to estimate the effectiveness of thromboprophylaxis, compare different agents, and identify any factors associated with effectiveness. METHODS: We undertook a systematic review and network meta-analysis (NMA) of randomized trials reporting VTE or bleeding outcomes that compared thromboprophylactic agents with each other or to no pharmacological prophylaxis, for this indication. An NMA was undertaken for each outcome or agent used, and a series of study-level network meta-regressions examined whether population characteristics, type of injury, treatment of injury, or duration of thromboprophylaxis were associated with treatment effect. RESULTS: Data from 6857 participants across 13 randomized trials showed that, compared with no treatment, low molecular weight heparin (LMWH) reduced the risk of any VTE (odds ratio [OR]: 0.52; 95% credible interval [CrI]: 0.37-0.71), clinically detected deep vein thrombosis (DVT) (OR: 0.39; 95% CrI: 0.12-0.94) and pulmonary embolism (PE) (OR: 0.16; 95% CrI: 0.01-0.74), whereas fondaparinux reduced the risk of any VTE (OR: 0.13; 95% CrI: 0.05-0.30) and clinically detected DVT (OR: 0.10; 95% CrI: 0.01-0.86), with inconclusive results for PE (OR: 0.40; 95% CrI: 0.01-7.53). CONCLUSIONS: Thromboprophylaxis with either fondaparinux or LMWH appears to reduce the odds of both asymptomatic and clinically detected VTE in people with temporary lower limb immobilization following an injury. Treatment effects vary by outcome and are not always conclusive. We were unable to identify any treatment effect modifiers other than thromboprophylactic agent used.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".