Comparison of Two Major Perioperative Bleeding Scores for Cardiac Surgery Trials
Bibliographic record
Abstract
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Research into major bleeding during cardiac surgery is challenging due to variability in how it is scored. Two consensus-based clinical scores for major bleeding: the Universal definition of perioperative bleeding and the European Coronary Artery Bypass Graft (E-CABG) bleeding severity grade, were compared in this substudy of the Transfusion Avoidance in Cardiac Surgery (TACS) trial. METHODS: As part of TACS, 7,402 patients underwent cardiac surgery at 12 hospitals from 2014 to 2015. We examined content validity by comparing scored items, construct validity by examining associations with redo and complex procedures, and criterion validity by examining 28-day in-hospital mortality risk across bleeding severity categories. Hierarchical logistic regression models were constructed that incorporated important predictors and categories of bleeding. RESULTS: E-CABG and Universal scores were correlated (Spearman ρ = 0.78, P < 0.0001), but E-CABG classified 910 (12.4%) patients as having more severe bleeding, whereas the Universal score classified 1,729 (23.8%) as more severe. Higher E-CABG and Universal scores were observed in redo and complex procedures. Increasing E-CABG and Universal scores were associated with increased mortality in unadjusted and adjusted analyses. Regression model discrimination based on predictors of perioperative mortality increased with additional inclusion of the Universal score (c-statistic increase from 0.83 to 0.91) or E-CABG (c-statistic increase from 0.83 to 0.92). When other major postoperative complications were added to these models, the association between Universal or E-CABG bleeding with mortality remained. CONCLUSIONS: Although each offers different advantages, both the Universal score and E-CABG performed well in the validity assessments, supporting their use as outcome measures in clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".