Perioperative Management of Antithrombotic Therapy in Patients Undergoing Plastic and Reconstructive Surgery: A Practical Tool Based on Current Guidelines
Bibliographic record
Abstract
Introduction: Given the aging population, plastic surgeons are increasingly faced with the challenge of balancing procedural bleeding risk with thromboembolic risk in patients receiving antithrombotic therapies undergoing elective procedures. Guideline recommendations remain unclear in this population, which contributes to heterogeneity in clinical practices. We present a practical approach that summarizes guideline recommendations to facilitate the perioperative management of patients requiring surgery who are already on antithrombotic agents. Methods: Due to the scarcity of plastic surgery-specific guidelines, recommendations were primarily adapted from the 2022 American College of Chest Physicians guidelines on perioperative management of antithrombotic therapy as they are recognized as authoritative and widely used in clinical practice. Results: A clinical practice conceptual framework was adapted based on preexisting guidelines, dividing decision-making into 3 steps: (1) assessing the procedural bleeding risk; (2) assessing the patients’ thromboembolic risk; and (3) determining appropriate management according to antithrombotic agent type. Specific indications are provided for continuing, stopping, and bridging anticoagulants and antiplatelet agents, as well as for consultation with a cardiologist or hematologist. Conclusion: The present framework can be implemented in plastic surgeons’ clinical practice to guide the management of patients on antithrombotic therapies, while minimizing nonessential referrals to the thrombosis service. The lack of plastic surgery-specific guidelines on this topic highlights a need for further research to “bootstrap” the risk categorization of plastic surgical procedures and their appropriate perioperative 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 | 0.027 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".