Agreement Among Cardiovascular Disease Risk Calculators
Bibliographic record
Abstract
BACKGROUND: Use of cardiovascular disease risk calculators is often recommended by guidelines, but research on consistency in risk assessment among calculators is limited. METHOD AND RESULTS: A search of PubMed and Google was performed. Five clinicians selected 25 calculators by independent review. Hypothetical patients were created with the use of 7 risk factors (age, sex, smoking, blood pressure, high-density lipoprotein, total cholesterol, and diabetes mellitus) dichotomized to high and low, generating 2(7) patients (128 total). These patients were assessed by each calculator by 2 clinicians. Risk estimates (and assigned risk categories) were compared among calculators. Selected calculators were from 8 countries, used 5- or 10-year predictions, and estimated either cardiovascular disease or coronary heart disease. With the use of 3 risk categories (low, medium, and high), the 25 calculators categorized each patient into a mean of 2.2 different categories, and 41% of unique patients were assigned across all 3 risk categories. Risk category agreement between pairs of calculators was 67%. This did not improve when analysis was limited to just the 10-year cardiovascular disease calculators. In nondiabetics, the highest calculated risk estimate from a calculator averaged 4.9 times higher (range, 1.9-13.3) than the lowest calculated risk estimate for the same patient. This did not change meaningfully for diabetics or when the analysis was limited to 10-year cardiovascular disease calculators. CONCLUSIONS: The decision as to which calculator to use for risk estimation has an important impact on both risk categorization and absolute risk estimates. This has broad implications for guidelines recommending therapies based on specific calculators.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
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".