Multidisciplinary Practice Variations of Anti-Thrombotic Strategies for Free Tissue Transfers
Bibliographic record
Abstract
Background: Venous thrombosis, the leading cause of free flap failure, may have devastating consequences. Many anti-thrombotic agents and protocols have been described for prophylaxis and treatment of venous thrombosis in free flaps. Methods: National surveys were distributed to microsurgeons (of both Plastics and ENT training) and hematology and thrombosis specialists. Data were collected on routine screening practices, perceived risk factors for flap failure, and pre-, intra-, and post-operative anti-thrombotic strategies. Results: There were 722 surveys distributed with 132 (18%) respondents, consisting of 102 surgeons and 30 hematologists. Sixty-five surgeons and 9 hematologists routinely performed or managed patients with free flaps. The top 3 perceived risk factors for flap failure according to surgeons were medical co-morbidities, past arterial thrombosis, and thrombophilia. Hematologists, however, reported diabetes, smoking, and medical co-morbidities as the most important risk factors. Fifty-four percent of physicians routinely used unfractionated heparin (UFH) or low-molecular-weight heparin (LMWH) as a preoperative agent. Surgeons routinely flushed the flap with heparin (37%), used UFH IV (6%), or both (8%) intra-operatively. Surgeons used a range of post-operative agents such as UFH, LMWH, aspirin, and dextran while hematologists preferred LMWH. There was variation of management strategies if flap thrombosis occurred. Different strategies consisted of changing recipient vessels, UFH IV, flushing the flap, adding post-operative agents, or a combination of strategies. Conclusions: There are diverse practice variations in anti-thrombotic strategies for free tissue transfers and a difference in perceived risk factors for flap failure that may affect patient management.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".