Heparin versus tirofiban in microvascular anastomosis: randomized controlled trial in a rat model.
Bibliographic record
Abstract
OBJECTIVE: Free flaps have become the mainstay of reconstruction after resection of head and neck cancer. Thrombosis after microvascular reanastamosis is a common reason for free flap failure. Various anticoagulants have been used topically and systemically to prevent thrombosis. Tirofiban is a glycoprotein IIb/IIIa inhibitor that prevents platelet aggregation and helps prevent thrombosis. The purpose of this study was to compare the thrombosis rate of topical heparin to topical heparin + tirofiban in a thrombogenic free flap model in rats. STUDY DESIGN: Prospective, randomized, double-blind, controlled trial. SUBJECTS AND METHODS: A thrombogenic free flap model was developed by raising a fasciocutaneous flap based on the epigastric artery in the Sprague-Dawley rat. An intimal flap was created proximal to the anastomosis site to increase thrombosis rates. Eighty rats were randomized to this thrombogenic free flap model using topical saline, heparin, or topical heparin + tirofiban. Each free flap was assessed for skin necrosis, capillary refill, and vessel thrombosis at 48 hours postprocedure. Data for each group were collected in a double-blind fashion. RESULTS: The heparin + tirofiban group had a 23% lower thrombosis rate and hence free flap failure rate when compared to the heparin alone group (p = .044). CONCLUSIONS: The use of topical tirofiban in addition to topical heparin in microvascular surgery results in reduced rates of thrombosis and should be considered for use in head and neck oncology patients undergoing free flap reconstruction.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".