Calibration and discrimination of the Framingham Risk Score and the Pooled Cohort Equations
Bibliographic record
Abstract
BACKGROUND: Although accurate risk prediction is essential in guiding treatment decisions in primary prevention of atherosclerotic cardiovascular disease, the accuracy of the Framingham Risk Score (recommended by a Canadian guideline) and the Pooled Cohort Equations (recommended by US guidelines) has not been assessed in a large contemporary Canadian population. Our primary objective was to assess the calibration and discrimination of the Framingham Risk Score and Pooled Cohort Equations in Ontario, Canada. METHODS: We conducted an observational study involving Ontario residents aged 40 to 79 years, without a history of atherosclerotic cardiovascular disease, who underwent cholesterol testing and blood pressure measurement from Jan. 1, 2010, to Dec. 31, 2014. We compared predicted event rates generated by the Framingham Risk Score and the Pooled Cohort Equations with observed event rates at 5 years using linkages from validated administrative databases. RESULTS: Our study cohort included 84 617 individuals (mean age 56.3 yr, 56.9% female). Over a maximum follow-up period of 5 years, we observed 2162 (2.6%) events according to the outcome definition of the Framingham Risk Score, and 1224 (1.4%) events according to the outcome definition of the Pooled Cohort Equations. The predicted event rate of 5.78% by the Framingham Risk Score and 3.51% by the Pooled Cohort Equations at 5 years overestimated observed event rates by 101% and 115%, respectively. The degree of overestimation differed by age and ethnicity. The C statistics for the Framingham Risk Score (0.74) and Pooled Cohort Equations (0.73) were similar. INTERPRETATION: The Framingham Risk Score and Pooled Cohort Equations significantly overpredicted the actual risks of atherosclerotic cardiovascular disease events in a large population from Ontario. Our finding suggests the need for further refinement of cardiovascular disease risk prediction scores to suit the characteristics of a multiethnic Canadian population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".