Risk Stratification Scores for Predicting Mortality in Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND: Four risk-stratification scores (RSSs - Euro, French, CCS/Higgins, Parsonnet) were tested as predictors of mortality in coronary artery bypass grafting (CABG) surgery. METHODS: From March to April 2000, the perioperative courses of 245 consecutive CABG patients were compared to the predictions according to the RSSs. Sensitivity and specificity were determined with receiver operating characteristics (ROC) curves. RESULTS: CCS/Higgins uses the most easily acquired patient data, and rates emergency conditions as high-risk. Euro focuses on advanced age and septal rupture. French uses the smallest number of patient parameters and rates rare critical situations as high-risk. Parsonnet is partially based on the physician's subjective assessment of a "catastrophic state," making the scoring arbitrary. All RSSs gave similar (not significant) areas under the ROC curves regarding mortality (Euro 0.826 +/- 0.080, French 0.783 +/- 0.094, CCS/Higgins 0.820 +/- 0.060, Parsonnet 0.831 +/- 0.042). Predicted risk levels for the 11 patients who died differed between the RSSs--Higgins placed these patients in 3 of 5 risk levels with ascending distribution. The other RSSs placed these patients in the highest risk level except for one and two patients, respectively, who were placed in the lowest Euro and French risk level. Euro and Parsonnet placed about half of all patients with non-lethal outcome in the highest risk level. CONCLUSIONS: All RSSs satisfactorily estimated the group risk for mortality. No RSS expressed sufficient validity to predict individuals with lethal outcome. In clinical use, CCS/Higgins proved the most practicable.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".