The Effect of Tranexamic Acid Administration During Liposuction on Bleeding Complications and Ecchymosis: A Systematic Review
Bibliographic record
Abstract
Liposuction is the most frequently performed cosmetic procedure. Tranexamic acid (TXA) has emerged as a promising blood loss reducing agent in plastic surgery, but its value in liposuction is still being studied. This systematic review investigates the safety and efficacy of TXA in reducing blood loss during liposuction procedures. A systematic review of PubMed, EMBASE, and Cochrane databases from inception to June 2023 was performed. The primary objective was to compare blood loss, hematoma rate, and ecchymosis from liposuction procedures in patients who received TXA with those who did not. The secondary objective was to assess the incidence of TXA-related complications. A total of 9 studies were included, published between 2018 and 2023, of which 8 were prospective and 1 was retrospective. A total of 345 intervention vs 268 control arms were compared. Follow-up time ranged from 1 to 14 days. Mean age and mean BMI ranged from 33 to 50 years and 23 to 30 kg/m2, respectively. Blood loss in aspirate was significantly less with TXA administration as assessed in 5 studies (P < .05). Of the 5 studies that described assessment of the incidence of ecchymosis, all reported less bruising with TXA use. Among all the studies, only 1 reported postoperative complications in 5 patients requiring transfusion in the control group (without TXA). The evidence provided in the literature suggests that TXA administration in liposuction is safe and effective for reducing blood loss and ecchymosis by both intravenous and local administration.
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 | 0.004 | 0.020 |
| 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) | 0.004 | 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".