AB124. SOH25_AB_369. Does the administration of topical tranexamic acid reduce haematoma rates in breast reductions: a systematic review
Bibliographic record
Abstract
Background: The risk of post op haematoma following breast reduction surgery ranges between 1% and 10%. Tranexamic is now used across surgical disciplines following excellent evidence supporting its efficacy at reducing bleeding. A survey of members of the British Association of Aesthetic Plastic Surgeons reports 83% of responders using tranexamic acid in breast reductions. However, there is unclear evidence relating to the efficacy of topical tranexamic acid use. Hence, this systematic review aims to address this. Methods: A priori protocol was registered with PROSPERO (registration CRD42024577863). Methodology was in keeping with the PRISMA guidelines for systematic reviews. Bias was assessed via Newcastle-Ottawa Scale and the Cochrane tool for assessing risk of bias in randomised trials. All eligible articles evaluating the impact of topical tranexamic acid on haematoma rates in patients undergoing breast reduction surgery were included. Analysis was conducted via a qualitative synthesis. Results: Overall, four studies including 789 breast reduction cases were included with 415 in the control groups and 374 receiving the intervention. There were 18 haematomas in the control group and five in the intervention group. Only one study reported statistically significant results supporting topical tranexamic acid administration. There was significant interstudy and intrastudy heterogeneity negating a meta-analysis. Conclusions: Intravenous tranexamic acid use in breast reduction surgery is widespread. However, there is limited evidence supporting the use of topical tranexamic acid. Outcomes between studies vary and further well-designed studies are required to provide clarity on the research question.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".