Prognostic factors associated with venous thromboembolism following traumatic injury: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Trauma patients are at increased risk of venous thromboembolism (VTE), including deep venous thrombosis and/or pulmonary embolism. We conducted a systematic review and meta-analysis summarizing the association between prognostic factors and the occurrence of VTE following traumatic injury. METHODS: We searched the Embase and Medline databases from inception to August 2023. We identified studies reporting confounding adjusted associations between patient, injury, or postinjury care factors and risk of VTE. We performed meta-analyses of odds ratios using the random-effects method and assessed individual study risk of bias using the Quality in Prognosis Studies tool. RESULTS: We included 31 studies involving 1,981,946 patients. Studies were predominantly observational cohorts from North America. Factors with moderate or higher certainty of association with increased risk of VTE include older age, obesity, male sex, higher Injury Severity Score, pelvic injury, lower extremity injury, spinal injury, delayed VTE prophylaxis, need for surgery, and tranexamic acid use. After accounting for other important contributing prognostic variables, a delay in the delivery of appropriate pharmacologic prophylaxis for as little as 24 to 48 hours independently confers a clinically meaningful twofold increase in incidence of VTE. CONCLUSION: These findings highlight the contribution of patient predisposition, the importance of injury pattern, and the impact of potentially modifiable postinjury care on risk of VTE after traumatic injury. These factors should be incorporated into a risk stratification framework to individualize VTE risk assessment and support clinical and academic efforts to reduce thromboembolic events among trauma patients. LEVEL OF EVIDENCE: Systematic Review and Meta-Analysis; Level III.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".