Tranexamic Acid Versus no Tranexamic Acid in Spinal Tumor and Metastasis Surgery—Meta-Analysis
Bibliographic record
Abstract
Study Design Meta-analysis. Objectives Spinal tumors and metastases remain a challenge for spine surgeons, with a significant risk of perioperative blood loss, which might require blood transfusions and lead to increased complications. Tranexamic acid (TXA) is an antifibrinolytic agent widely used in various surgical procedures; however, its efficacy in spinal oncology surgery remains unclear. This meta-analysis aims to evaluate the clinical effectiveness of TXA on perioperative outcomes, mainly blood loss, among patients with oncological spines undergoing surgery. Methods PubMed, Scopus, and Web of Science were systematically searched from inception for eligible articles. We included studies assessing TXA vs no TXA or placebo on perioperative outcomes among patients with spinal tumors or metastases. Results After a comprehensive search, seven studies were included. Blood loss (MD −111.75 mL; 95% CI −217.08 to −6.43; P = 0.04; I 2 = 83.8%), postoperative drain (MD −90.49 mL; 95% CI −150.17 to −30.81; P < 0.01; I 2 = 68.1%), hospitalization duration (MD −1.88 days; 95% CI −3.33 to −0.44; P = 0.004; I 2 = 0%), and overall complication rate (OR 0.54; 95% CI 0.34 to 0.87; P = 0.011; I 2 = 0.0) were significantly reduced in TXA group. There was no significant difference in operation time, transfusions, or thrombosis events. Conclusion Retrospective data suggest TXA may reduce blood loss in spinal tumor surgery. However, its effect on hospital stay and complications remains uncertain. Despite appearing safe, evidence remains limited by bias and heterogeneity. High-quality RCTs are needed to confirm its efficacy and define clinical guidelines.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.066 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".