S1434 Efficacy and Safety of Tranexamic Acid in Acute Gastrointestinal Bleeding: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Acute gastrointestinal bleeding (AGIB) is a significant cause of emergency admissions associated with high morbidity and mortality. Tranexamic acid (TXA), an antifibrinolytic agent, has been proposed for controlling AGIB but concerns remain regarding its safety and effectiveness. This meta-analysis aimed to assess the safety and efficacy of TXA in AGIB. Methods: Electronic databases like PubMed, Cochrane Library, and ScienceDirect were searched from inception till January 2025. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Risk Ratios (RR) and Mean Differences (MD) along with 95% confidence intervals (CI) were pooled under the random effects model using Review Manager version 5.4.1 for the dichotomous and continuous outcomes. The quality was assessed by the Cochrane RoB 2.0 tool and the Newcastle Ottawa Scale. Publication bias was assessed by the funnel plots and Egger’s regression test. Results: Eighteen studies pooling a total of 15,014 patients were included in this meta-analysis. Mortality was significantly reduced in the TXA group with a pooled RR of 0.83 (95% CI: [0.73, 0.95]; P = 0.008; I2 = 0%). Rebleeding risk was also significantly decreased in the TXA arm (RR = 0.75; 95% CI: [0.62, 0.92]; P = 0.005; I2 = 41%). The transfusion requirement (RR = 0.99; 95% CI: [0.93, 1.06]; P = 0.74; I2 = 37%) and the need for overall interventions (RR = 0.87; 95% CI: [0.69, 1.10]; P = 0.25; I2 = 38%) showed no significant difference between the 2 arms . Other outcomes including the need for therapeutic endoscopic intervention (RR = 0.86; 95% CI: [0.62, 1.21]; P = 0.39; I2 = 50%), need for surgical intervention (RR = 0.76; 95% CI: [0.54, 1.07]; P = 0.11; I2 = 47%), venous thromboembolic events (RR = 1.29; 95% CI: [0.53, 3.16]; P = 0.58; I2 = 39%), arterial thromboembolic events (RR = 0.95; 95% CI: [0.64, 1.42]; P = 0.82; I2 = 0%), transfusion volume (MD = -0.64; 95% CI: [-1.37, 0.08]; P = 0.08; I2 = 77%), and length of hospital stay (MD = -0.17; 95% CI: [-0.48, 0.14]; P = 0.28; I2 = 42%) were comparable between the 2 groups. Conclusion: Tranexamic acid significantly reduces mortality and rebleeding in AGIB, but shows no significant differences in transfusion requirements, interventions, or thromboembolic risks, indicating potential benefits with comparable safety.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".