Comparison of cardiovascular disease risk calculators
Bibliographic record
Abstract
PURPOSE OF REVIEW: The cardiovascular benefit of many preventive interventions (like statins) is strongly dependent on the baseline cardiovascular risk of the patient. Many lipid and vascular primary prevention guidelines advocate for the use of cardiovascular risk calculators. RECENT FINDINGS: There are over 100 cardiovascular risk prediction models, and some of these models have spawned scores of calculators. Only about 25 of these models/calculators have been externally validated. The ability to identify who will have events frequently varies little (<5%) between models. However, disagreement between risk calculators is common with one in three paired comparisons disagreeing on risk category. In part, this disagreement is because calculators vary according to the database they are derived from, choice of clinical endpoints and risk interval duration upon which the estimate is based. Additional risk factors do little to improve the basic risk predictions performance, except perhaps coronary artery calcium which still requires further study before regular use. SUMMARY: The estimates provided by cardiovascular risk calculators are ballpark approximations and have a margin of error. Physicians should use models derived from, or calibrated for, populations similar to theirs and understand the endpoints, duration, and special features of their selected calculator.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".