Tranexamic acid in cardiac surgery: a systematic review and meta-analysis (protocol)
Bibliographic record
Abstract
INTRODUCTION: Bleeding during cardiac surgery is associated with increased morbidity and mortality. Tranexamic acid is an antifibrinolytic with proven efficacy in major surgeries. Current clinical practice guidelines recommend intraoperative use in cardiac procedures. However, several complications have been reported with tranexamic acid including seizures. This review intends to summarise the evidence examining the efficacy and safety of tranexamic acid in patients undergoing cardiac surgery. METHODS/DESIGN: We will search MEDLINE, Embase, PubMED, ACPJC, CINAHL and the Cochrane trial registry for eligible randomised controlled trials, the search dates for all databases will be from inception until 1 January 2019, investigating the perioperative use of topical and/or intravenous tranexamic acid as a stand-alone antifibrinolytic agent compared with placebo in patients undergoing open cardiac surgery. We categorised outcomes as patient critical or patient important. Selected patient-critical outcomes are: mortality (intensive care unit, hospital and 30-day endpoints), reoperation within 24 hours, postoperative bleeding requiring transfusion of packed red blood cells, myocardial infarction, stroke, pulmonary embolism, bowel infarction, upper or lower limb deep vein thrombosis and seizures. Those outcomes, we perceived as clinical experts to be most patient valued and patients were not involved in outcomes selection process. We will not apply publication date, language, journal or methodological quality restrictions. Two reviewers will independently screen and identify eligible studies using predefined eligibility criteria and then review full reports of all potentially relevant citations. A third reviewer will resolve disagreements if consensus cannot be achieved. We will present the results as relative risk with 95% CIs for dichotomous outcomes and as mean difference or standardised mean difference for continuous outcomes with 95% CIs. We will assess the quality of evidence using the Grading of Recommendations, Assessment, Development and Evaluation approach. ETHICS AND DISSEMINATION: Formal ethical approval is not required as primary data will not be collected. The results will be disseminated through a peer-reviewed publication TRIAL REGISTRATION NUMBER: CRD42018105904.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.003 |
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".