The efficacy and safety of tranexamic acid use during radical cystectomy
Bibliographic record
Abstract
INTRODUCTION: Radical cystectomy (RC) is the standard treatment for muscle-invasive bladder cancer (MIBC), but it is associated with substantial perioperative blood loss and high transfusion rates. Tranexamic acid (TXA), an antifibrinolytic agent, has demonstrated efficacy in reducing surgical blood loss across various specialties; however, due to a paucity of randomized controlled trials in the RC setting, its role remains uncertain. We conducted a systematic review and meta-analyses to synthesize the current evidence and provide a critical assessment of TXA use during RC. METHODS: Studies evaluating TXA during RC were identified through a comprehensive search of multiple databases up to November 2024. Primary outcomes included intraoperative and perioperative blood transfusion rates and estimated blood loss. The secondary outcome assessed was thromboembolic events RESULTS: Five studies comprising 1736 patients were included. TXA did not significantly reduce estimated blood loss (MD: -85.56 mL; 95% confidence interval [CI] -191.13-20.02, p>0.05) or intraoperative transfusion rates (odds ratio [OR] 0.73, 95% CI 0.40-1.33, p>0.05); however, TXA was associated with a lower likelihood of perioperative transfusions (OR 0.56, 95% CI 0.32-0.97, p<0.05). Notably, TXA increased the risk of thromboembolic events (OR 2.05, 95% CI 1.15-4.65, p<0.05). Heterogeneity varied across analyses, with robotic-assisted RC underrepresented in the included studies. CONCLUSIONS: This systematic review and meta-analysis revealed that, in patients undergoing RC, the use of TXA does not significantly reduce estimated blood loss or intraoperative transfusion rates. Moreover, TXA appears to be associated with an increased incidence of thromboembolic events, suggesting a potential pro-thrombotic effect. Based on these findings, its routine use in this context cannot be recommended, particularly when intended to reduce thromboembolic risk.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".