The Cardiac Anesthesia Risk Evaluation Score
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Bibliographic record
Abstract
BACKGROUND: The Cardiac Anesthesia Risk Evaluation (CARE) score is a simple risk classification for cardiac surgical patients. It is based on clinical judgment and three clinical variables: comorbid conditions categorized as controlled or uncontrolled, surgical complexity, and urgency of the procedure. This study compared the CARE score with the Parsonnet, Tuman, and Tu multifactorial risk indexes for prediction of mortality and morbidity after cardiac surgery. METHODS: In this prospective study, 3,548 cardiac surgical patients from one institution were risk stratified by two investigators using the CARE score and the three tested multifactorial risk indexes. All patients were also given a CARE score by their attending cardiac anesthesiologist. The first 2,000 patients served as a reference group to determine discrimination of each classification with receiver operating characteristic curves. The following 1,548 patients were used to evaluate calibration using the Pearson chi-square goodness-of-fit test. RESULTS: The areas under the receiver operating characteristic curves for mortality and morbidity were 0.801 and 0.721, respectively, with the CARE score rating by the investigators; 0.786 and 0.710, respectively, with the CARE score rating by the attending anesthesiologists (n = 8); 0.808 and 0.726, respectively, with the Parsonnet index; 0.782 and 0.697, respectively, with the Tuman index; 0.770 and 0.724 with the Tu index, respectively. All risk models had acceptable calibration in predicting mortality and morbidity, except for the Parsonnet classification, which failed calibration for morbidity (P = 0.026). CONCLUSIONS: The CARE score performs as well as multifactorial risk indexes for outcome prediction in cardiac surgery. Cardiac anesthesiologists can integrate this score in their practice and predict patient outcome with acceptable accuracy.
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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 | 0.002 | 0.001 |
| 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 it