Perioperative Management Of Anticaogulated Patients In Oral Surgery: Tranexamic Acid
Bibliographic record
Abstract
Introduction Managing anticoagulated patients in oral surgery requires balancing bleeding risk with thromboembolic prevention. Discontinuing anticoagulants increases thrombotic risk, while maintaining them may lead to excessive bleeding. Topical 4.8% tranexamic acid (TXA), an antifibrinolytic agent, stabilizes clot formation without affecting systemic coagulation. This case highlights the use of TXA as a local hemostatic measure in an anticoagulated patient undergoing tooth extraction for implant placement. Case description A male patient on anticoagulants required extraction before dental implant placement. • Preoperative: No anticoagulation adjustment; TXA rinse (4.8%, 2 min). • Intraoperative: Atraumatic extraction, Gentle irrigation with TXA, TXA-soaked gauze applied. • Postoperative: TXA mouth rinses (10 mL, 2 min, 4×/day for 2 days). Follow-ups at 24 hours, 1 week, and 2 weeks. Discussion Hemostasis was achieved without excessive bleeding or complications. Topical TXA effectively controlled bleeding while allowing continuous anticoagulation, aligning with current evidence-based guidelines. Studies support TXA’s role in reducing oral surgery bleeding without systemic risks. Conclusion/clinical significance 4.8% TXA is a safe, effective hemostatic adjunct for anticoagulated patients undergoing oral surgery. Its use eliminates the need for anticoagulant discontinuation, minimizing both thrombotic and bleeding risks. This case supports TXA as a standard hemostatic strategy in oral surgery, warranting further study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".