Evaluation of Polytetrafluoroethylene Micrografts in Microvascular Surgery
Bibliographic record
Abstract
Interposition grafts are an important and invaluable technique in free microvascular tissue transfer as they extend the vascular pedicle to allow it to reach undisturbed donor vessels away from the surgical or traumatic site. Autologous vein has been the most commonly used material for interposition grafting, but it has significant disadvantages. Nonautologous grafts are readily available and could certainly remedy most of the autologous graft disadvantages. This study was designed to (1) evaluate the patency of expanded polytetrafluoroethylene (PTFE) grafts in a simple microvascular vessel repair and (2) evaluate the survival of microvascular free flaps using PTFE arterial interposition grafts. The inferior epigastric vein or PTFE was used to reconstruct the femoral artery in 10 animals (20 sides) in a simple vessel repair model and in 20 animals in a groin free flap model. All epigastric vein grafts were patent at 1 week in the vessel repair group. Only five in the PTFE vessel repairs were patent. Nine of 10 free flaps were viable in the free flap group with epigastric vein interposition. All of the 10 free flaps with PTFE interposition failed. There was a statistically significant difference in vessel patency between PTFE grafts in the vessel repair and free flap models. Our data show that PTFE microvascular grafts are a poor substitute for vein grafts in the vessel repair and free flap animal models. We also found that simple vessel repair is not an acceptable model to study manipulations intended for free tissue transfer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".