Effectiveness and Safety of Topically Applied Tranexamic Acid with Epinephrine in Surgical Procedures: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Topical tranexamic acid (TXA), often combined with epinephrine, is used to reduce perioperative bleeding. This systematic review evaluates the safety and effectiveness of this combination across surgical procedures. METHODS: A comprehensive search of eight databases was conducted from inception to 26 June 2025. Studies were eligible if they compared topically or locally applied TXA with epinephrine to epinephrine alone in surgical patients. Animal studies, case reports, non-English publications, and studies without comparators were excluded. Screening, data extraction, and risk of bias assessments followed PRISMA guidelines. RESULTS: Ten studies met inclusion criteria (four randomized and six non-randomized), covering burn surgery, rhytidectomy, liposuction, septoplasty, endoscopic sinus surgery, dacryocystorhinostomy, and joint arthroplasty. TXA was applied topically or via tumescent infiltration. Most studies reported reduced intraoperative blood loss, improved surgical field visibility, lower drain output, shorter hemostasis time, and reduced transfusion rates. No increase in thromboembolic or major complications was observed. CONCLUSION: The combination of TXA and epinephrine appears safe and maybe effective for perioperative bleeding control. However, heterogeneity in dosing and outcomes limits generalizability. Further research is needed to standardize protocols and confirm long-term safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".