Kidney disease in trials of perioperative tranexamic acid.
Bibliographic record
Abstract
STUDY OBJECTIVE: To assess how kidney disease is handled in randomized trials evaluating the safety and efficacy of perioperative tranexamic acid, and to evaluate its effects across levels of kidney function. DESIGN: Systematic review and meta-analysis of randomized controlled trials. SETTING: We screened studies from a previous comprehensive systematic review, and updated its search of PubMed, Embase, and Cochrane CENTRAL to July 31, 2023. PATIENTS: Patients undergoing non-obstetric surgery. INTERVENTIONS: Intravenous tranexamic acid compared to placebo or usual care without tranexamic acid. MEASUREMENT: We summarized the handling of kidney disease in eligibility criteria, dose adjustments for kidney function, and effects of tranexamic acid on thrombotic events, seizures, and bleeding by subgroups of kidney function. MAIN RESULTS: (RR, 1.00; 95% CI, 0.91 to 1.11; P for subgroup difference = 0.47), but both trials excluded patients with severe kidney disease. No analysis could be performed regarding seizure risk. One large trial in noncardiac surgery reported similar reduction in bleeding across subgroups of kidney function but excluded patients with creatinine clearance <30 mL/min. CONCLUSIONS: The large evidence base supporting perioperative tranexamic acid suffers from broad and unjustified exclusion of patients with kidney disease. Typical perioperative dosing of tranexamic acid is likely safe and effective in patients with creatinine clearance >30 mL/min, but effects in more severe kidney disease are unknown.
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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 | 0.046 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".