Tranexamic acid reduces perioperative blood transfusions following open radical cystectomy – a propensity-score matched analysis
Bibliographic record
Abstract
PURPOSE: Radical cystectomy is associated with bleeding and high transfusion rates, presenting challenges in patient management. This study investigated the prophylactic use of tranexamic acid during radical cystectomy. METHODS: All consecutive patients treated with radical cystectomy at a tertiary care university center were included from a prospectively maintained database. After an institutional change in the cystectomy protocol patients received 1 g of intravenous bolus of tranexamic acid as prophylaxis. To prevent bias, propensity score matching was applied, accounting for differences in preoperative hemoglobin, neoadjuvant chemotherapy, tumor stage, and surgeon experience. Key outcomes included transfusion rates, complications, and occurrence of venous thromboembolism. RESULTS: In total, 420 patients were included in the analysis, of whom 35 received tranexamic acid. After propensity score matching, 32 patients and 32 controls were matched with regard to clinicopathologic characteristics. Tranexamic acid significantly reduced the number of patients who received transfusions compared to controls (19% [95%-Confidence interval = 8.3; 37.1] vs. 47% [29.8; 64.8]; p = 0.033). Intraoperative and postoperative transfusion rates were lower with tranexamic acid, though not statistically significant (6% [1.5; 23.2] vs. 19% [8.3; 37.1], and 16% [6.3; 33.7] vs. 38% [21.9; 56.1]; p = 0.257 and p = 0.089, respectively). The occurrence of venous thromboembolism did not differ significantly between the groups (9% [2.9; 26.7] vs. 3% [0.4; 20.9]; p = 0.606). CONCLUSION: Prophylactic tranexamic administration, using a simplified preoperative dosing regimen of 1 g as a bolus, significantly lowered the rate of blood transfusion after cystectomy. This exploratory study indicates the potential of tranexamic acid in enhancing outcomes of open radical cystectomy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".