Mortality risk prediction in high-risk patients undergoing coronary artery bypass grafting: Are traditional risk scores accurate?
Bibliographic record
Abstract
BACKGROUND: The performance of traditional scores is significantly limited to predict mortality in high-risk cardiac surgery. The aim of this study was to compare the performance of STS, ESII and HiriSCORE models in predicting mortality in high-risk patients undergoing CABG. METHODS: Cross-sectional analysis in the international prospective database of high-risk patients: HiriSCORE project. We evaluated 248 patients with STS or ESII (5-10%) undergoing CABG in 8 hospitals in Brazil and China. The main outcome was mortality, defined as all deaths occurred during the hospitalization in which the operation was performed, even after 30 days. Five variables were selected as predictors of mortality in this cohort of patients. The model's performance was evaluated through the calibration-in-the-large and the receiver operating curve (ROC) tests. RESULTS: The mean age was 69.90±9.45, with 52.02% being female, 25% of the patients were on New York Heart Association (NYHA) class IV and 49.6% had Canadian Cardiovascular Society (CCS) class 4 angina, and 85.5% had urgency or emergency status. The mortality observed in the sample was 13.31%. The HiriSCORE model showed better calibration (15.0%) compared to ESII (6.6%) and the STS model (2.0%). In the ROC curve, the HiriSCORE model showed better accuracy (ROC = 0.74) than the traditional models STS (ROC = 0.67) and ESII (ROC = 0.50). CONCLUSION: Traditional models were inadequate to predict mortality of high-risk patients undergoing CABG. However, the HiriSCORE model was simple and accurate to predict mortality in high-risk patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".