Reverse Venous Arterialization Simplified Technique: A Novel Method to Correct an Inadvertently Created Femoral Artery to Peroneal Vein Bypass
Bibliographic record
Abstract
Chronic limb-threatening ischemia (CLTI) presents a significant management challenge. We describe the case of an 80-year-old female with CLTI and dry gangrene who underwent a common femoral artery (CFA)-to-posterior tibial artery (PTA) bypass that remained patent but was inadvertently anastomosed to the peroneal vein, resulting in venous arterialization. Postoperatively, her gangrene progressed and pain worsened, prompting an offer of below-knee amputation. Upon presentation for a second opinion, angiography confirmed the patent but malpositioned bypass. Given her extensive comorbidities, she was considered a poor candidate for open revision. We report successful percutaneous correction using the venous arterialization simplified technique (VAST), which re-established flow from the bypass graft back into the arterial lumen of the PTA using a double-gunsight approach. A 5 × 100 mm Viabahn stent graft was deployed to bridge the bypass and PTA, followed by angioplasty of the PTA and plantar artery, restoring in-line perfusion to the plantar arch and digital runoff. At 2-month follow-up, the patient demonstrated marked wound healing and avoided amputation. This case underscores the role of innovative endovascular rescue techniques in high-risk patients with preserved distal runoff and limited surgical options.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".