The Efficacy and Safety of Tranexamic Acid in the Management of Perioperative Bleeding After Percutaneous Nephrolithotomy: A Systematic Review and Meta-Analysis of Comparative Studies
Bibliographic record
Abstract
Introduction: We performed a systematic review and meta-analysis of the current literature to assess the efficacy and safety of tranexamic acid (TXA) in the management of postoperative bleeding after percutaneous nephrolithotomy (PCNL). Methods: A systematic literature review was performed in March 2021. Two reviewers independently screened, identified, and evaluated comparative studies assessing the effectiveness of TXA in preventing bleeding after PCNL when compared with placebo or no intervention. The incidence of transfusion, complete stone clearance, and complications were extracted among TXA and control groups to generate the risk ratio (RR) and corresponding 95% confidence interval (CI). Blood loss, hemoglobin (Hb) drop, length of hospital stays, and operative (OR) time were analyzed using standard mean difference (SMD) with corresponding 95% CI. Effect estimates were pooled using the inverse-variance approach with a random-effect model. Results: A total of 11 studies (8 randomized controlled trial, 1 prospective cohort, and 2 retrospective cohort studies; total 1842 patients) of low-to-moderate-quality were included in the meta-analysis. Overall pooled effect estimates demonstrated a decreased transfusion rate (RR 0.36; 95% CI 0.25 to 0.51), blood loss (SMD −0.74; 95% CI −1.14 to −0.34), and Hb drop (SMD −0.95; 95% CI −1.51 to −0.39) among patients in the TXA group when compared with those in the control. The number needed to treat was 11 to prevent one transfusion. Patients who received TXA also had improved stone clearance (RR 1.08; 95% CI 1.02 to 1.14), lower minor (RR 0.72; 95% CI 0.58 to 0.89) and major (RR 0.38; 95% CI 0.21 to 0.69) complications, shorter hospital stays (SMD −0.52; 95% CI −1.01 to −0.04) and decreased OR time (SMD −0.89; 95% CI −1.46 to −0.31). Conclusions: TXA can effectively reduce postoperative bleeding after PCNL. Future studies should identify a subset of patients who may benefit from preoperative TXA administration for PCNL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".