Tranexamic acid use in sarcoma surgery patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Perioperative bleeding increases morbidity and mortality in sarcoma patients. Tranexamic acid (TXA), an antifibrinolytic, is widely utilized in non-sarcoma orthopaedic surgeries, but its adoption in sarcoma surgery is hindered by concerns about thrombotic events. METHODS: Searches in Ovid MEDLINE, EMBASE, and CENTRAL were performed without date restrictions. Inclusion criteria encompassed sarcoma patients undergoing surgery with TXA intervention. Two authors independently screened studies, resolved conflicts, and assessed biases. RESULTS: Eight studies met inclusion criteria, comprising 2142 patients. TXA administration varied in dose and timing across studies. Meta-analysis revealed significantly reduced mean blood loss with TXA of -462.5 mL ([95% confidence interval [CI: -596.7, -328.31], p < 0.001) but no difference in transfusion rates (odds ratio [OR] = 0.51 [95% CI: 0.14-1.89]) or venous thromboembolism events (OR = 0.93 [95% CI: 0.40, 2.16]). Study biases were predominantly moderate to high due to retrospective designs and lack of control for confounders. Quality of reporting varied, with limitations identified in outcome reporting and effect size estimation. CONCLUSIONS: Despite evidence of reduced blood loss, the absence of prospective studies limits conclusive recommendations on TXA use in sarcoma surgery. Further research is warranted to determine optimal TXA regimens and assess safety concerns regarding thrombotic events in this patient population.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".