Venous Thromboembolism in Lower Extremity Amputees: A Systematic Review of the Literature
Bibliographic record
Abstract
Patients undergoing lower extremity amputation (LEA) are at risk of developing deep venous thrombosis (DVT) and pulmonary embolism (PE), but no generally accepted prevention guidelines exist. This systematic review aimed at understanding the incidence of VTE with or without thromboprophylaxis in adult patients with major lower extremity amputation (LEA). Primary outcomes were onset of DVT, PE, or mortality. Secondary outcomes were any major adverse events due to treatment. We searched English language full-text papers in multiple databases using keywords, including amputation/adverse effects, amputation/complications, venous thromboembolism, deep vein thrombosis, and pulmonary embolism. Twenty-eight studies providing observations for 4,841 patients were selected. The fatal PE risk was 2.6% without prophylaxis and significantly decreased to a non-zero residual risk of 0.9% with VTE prophylaxis. Above-knee amputees were at greatest risk of VTE and subsequent complications. The risk was not confined to the amputated stump and can involve the contralateral limb. The role of compression ultrasonography screening in asymptomatic patients remains controversial in various populations at risk for VTE. All patients undergoing major LEA should be considered at high risk for the development of VTE, even after discharge from hospital. We recommend prophylactic anticoagulation (if not contraindicated) and clinical surveillance in all patients undergoing LEA and further studies to determine the optimal prophylactic strategy. Disclosures No relevant conflicts of interest to declare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".