Effect of Tranexamic Acid on Bleeding Outcomes After Percutaneous Nephrolithotomy: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Purpose: We performed a systematic review and meta-analysis of the literature to evaluate the efficacy of the routine use of tranexamic acid (TXA) during percutaneous nephrolithotomy (PCNL). Methods: This systematic review was conducted following the updated reporting guidelines from PRISMA 2020. Results: In total, 275 titles and abstracts were reviewed, of which 20 were screened to be eligible for full text review. Of these 20 articles, 11 were selected for inclusion after full article evaluations. Seven of these 11 studies were seen as having a low risk of bias with a Jadad score of ≥3. These studies were included for data extraction. Once data were extracted, 964 patients were included. The primary outcome, blood transfusion rate, showed significant reduction with a ratio for transfusion rate of 0.34 [95% confidence interval (CI) (0.19 to 0.61), z = 3.61, p = 0.0003]. Mean hemoglobin (Hgb) drop and operative time were both shown to be reduced with the use of TXA. The mean difference for Hgb drop was −0.86 [95% CI (−1.26 to −0.46), z = 4.23, p < 0.0001]. Reduction in operative time showed a mean difference of −8.45 minutes [95% CI (−15.04 to −1.86), z = 2.51, p = 0.01]. Stone clearance was not shown to differ significantly between experimental and control groups, with a risk ratio of 1.28 [95% CI (0.89 to 1.84), z = 1.31, p = 0.19]. Conclusions: This meta-analysis revealed that the routine use of TXA at time of PCNL reduces the rates of blood transfusion, mean Hgb drop, and operative time. With the low cost of TXA and strong safety profile, stronger consideration should be given to the routine use of TXA during PCNL by endoscopic surgeons.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".