Validation of the Canada Acute Coronary Syndrome Risk Score for Hospital Mortality in the Gulf Registry of Acute Coronary Events‐2
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Bibliographic record
Abstract
BACKGROUND: Several risk scores have been developed for acute coronary syndrome (ACS) patients, but their use is limited by their complexity. The new Canada Acute Coronary Syndrome (C-ACS) risk score is a simple risk-assessment tool for ACS patients. This study assessed the performance of the C-ACS risk score in predicting hospital mortality in a contemporary Middle Eastern ACS cohort. HYPOTHESIS: The C-ACS score accurately predicts hospital mortality in ACS patients. METHODS: The baseline risk of 7929 patients from 6 Arab countries who were enrolled in the Gulf RACE-2 registry was assessed using the C-ACS risk score. The score ranged from 0 to 4, with 1 point assigned for the presence of each of the following variables: age ≥75 years, Killip class >1, systolic blood pressure <100 mm Hg, and heart rate >100 bpm. The discriminative ability and calibration of the score were assessed using C statistics and goodness-of-fit tests, respectively. RESULTS: The C-ACS score demonstrated good predictive values for hospital mortality in all ACS patients with a C statistic of 0.77 (95% confidence interval [CI]: 0.74-0.80) and in ST-segment elevation myocardial infarction and non-ST-segment elevation acute coronary syndrome patients (C statistic: 0.76, 95% CI: 0.73-0.79; and C statistic: 0.80, 95% CI: 0.75-0.84, respectively). The discriminative ability of the score was moderate regardless of age category, nationality, and diabetic status. Overall, calibration was optimal in all subgroups. CONCLUSIONS: The new C-ACS score performed well in predicting hospital mortality in a contemporary ACS population outside North America.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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