P5350Potential utility of the SCORE risk estimator to predict fatal cardiovascular events in a North American population: CARTaGENE cohort
Bibliographic record
Abstract
Background: The SCORE risk estimator for fatal cardiovascular (CV) events has been well validated in European regions at low- and high-risk for CV events. However, its applicability in North-American populations has not been verified. We sought to examine the predictive value of SCORE in patients without prior CV disease in Quebec, Canada. Methods: The CARTaGENE cohort was a cross-sectional study randomly selected participants in Quebec (2009–2010). We included patients only from 40–70 years and excluded patients with prior CV history for this analysis. We censored fatal CV events by using the provincial health administrative datasets. We computed the cumulative 5-year CV death rate to determine the CV risk profile of our cohort and the applicable SCORE risk estimator. Finally, we evaluated the discrimination of the model by computing the area under the receiver-operating curves (AUC). Results: There were 19,599 subjects, 52% females with a mean age of 54.2 years. The mean LDL, HDL, TC/HDL ratio were 3.0, 1.2, 4.5 mmol/L respectively. The mean glycated hemoglobin was 5.7%. There were 0.6% of patients with diabetes mellitus type 1 and 7.0% patients with diabetes mellitus type 2 The 5-year incidence rates of stroke and myocardial infarction were 0.4% and 0.7%, respectively. At 5 years, there were 252 deaths including 35 of CV causes (257 and 36 per 100,000 person-year, respectively). The cumulative 5-year CV death rate was 0.17% which was similar to the European regions at low risk for CV deaths. The low-risk SCORE estimator performed well to predict 5-year CV death in our cohort with an AUC of 0.75 (95% confidence intervals: 0.67–0.83) (Figure 1)
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".