Vascular Injury Patterns in High-Energy Trauma: A Systematic Review of Incidence, Diagnostic Modalities, and Management Strategies
Bibliographic record
Abstract
Vascular injuries in high-energy trauma are life- and limb-threatening emergencies, requiring prompt diagnosis and intervention. This systematic review included five studies published between 2019 and 2023, comprising approximately 480 patients. A literature search of PubMed/MEDLINE, Embase, Scopus, and the Cochrane Library was conducted using keywords such as "vascular injury," "high-energy trauma," "CT angiography," and "endovascular repair." Most studies focused on extremity injuries, particularly involving the femoral, popliteal, iliac, and brachial arteries. CT angiography (CTA) consistently demonstrated superior diagnostic accuracy compared to Doppler ultrasonography or clinical examination. Endovascular techniques, such as stenting and embolization, were compared with open surgical repair. Outcomes assessed included amputation rates, limb salvage, mortality, and hospital stay. Endovascular methods showed potential benefits in reducing complications and hospital duration in selected patients. Common pathophysiological patterns included arterial disruption, thrombosis, and ischemia-reperfusion injury. However, none of the studies evaluated renal outcomes. The included studies were assessed for risk of bias using ROBINS-I (Risk of Bias in Non-randomized Studies of Interventions) and the Newcastle-Ottawa Scale (NOS), revealing moderate quality overall. These findings highlight the diagnostic value of CTA and suggest a growing role for endovascular management in high-energy vascular trauma. Further high-quality studies are needed to guide treatment selection and optimize outcomes.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".