The Effects of Tranexamic Acid in Breast Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Tranexamic acid (TXA) is used in trauma and surgical settings. Its role in reducing postoperative blood loss in breast surgery remains unclear. The primary objective of this study was to determine the effect of TXA on postoperative blood loss in breast surgery. METHODS: Searches of the PubMed, Ovid MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials databases were performed from inception to April 3, 2020. Inclusion criteria were any retrospective reviews, prospective cohort studies, and randomized controlled trials that administered TXA (topical or intravenously) in the context of breast surgery. Quality of studies were evaluated using the risk of bias in randomized trials tool and the risk of bias in nonrandomized studies of interventions tool. Data were pooled, and a meta-analysis was performed. RESULTS: In total, seven studies were included, representing 1226 patients (TXA, 632 patients; control, 622 patients). TXA was administered as follows: topically (20 mL of 25 mg/mL TXA intraoperatively; n =258 patients), intravenously (1 to 3 g perioperatively; n = 743 patients), or both (1 to 3 g daily up to 5 days postoperatively; n = 253 patients). TXA administration reduced hematoma formation in breast surgery (risk ratio, 0.48; 95% CI, 0.32 to 0.73), with no effect on drain output (mean difference, -84.12 mL; 95% CI, -206.53 to 38.29 mL), seroma formation (risk ratio, 0.92; 95% CI, 0.60 to 1.40), or infection rates (risk ratio, 1.01; 95% CI, 0.46 to 2.21). No adverse effects were reported. CONCLUSION: The use of TXA in breast surgery is a safe and effective modality with low-level evidence that it reduces hematoma rates without affecting seroma rates, postoperative drain output, or infection rates.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".