Variation among cardiovascular risk calculators in relative risk increases with identical risk factor increases
Bibliographic record
Abstract
BACKGROUND: Risk estimates for the same patient can vary substantially among cardiovascular risk calculators and the reasons are not fully explained. We compared the relative risk increases for consistent risk factors changes across different cardiovascular risk calculators. METHODS: Five clinicians independently selected 16 calculators providing absolute risk estimations. Hypothetical patients were generated using a combination of seven risk factors [age, gender, smoking, blood pressure, high-density lipoprotein (HDL), total cholesterol and diabetes] dichotomized to high and low risk, generating 2(7) patients (128 total). Relative risk increases due to specific risk factors were determined and compared. RESULTS: The 16 selected calculators were from six countries, used 5- and 10-year predictions, and estimated CVD or coronary heart disease risk. Across the different calculators for non-diabetic patients, changing age from 50 to 70 produced average relative risk increases from 82 to 395%, gender (female to male) 35-225%, smoking status 31-118%, systolic blood pressure (120-160 mmHg) 16-124%, total cholesterol (4-7 mmol/L) 51-302% and HDL (1.3-0.8 mmol/L) 27-133%. Similar results were found among diabetic patients. Some calculators appeared to have consistently higher relative risk increases over multiple risk factors. CONCLUSIONS: Cardiovascular risk calculators weigh the same risk factors differently. For each risk factor, the relative risk increase from the calculator with the highest increase was generally three to eight times greater than the relative risk increase from the calculator with lowest increase. This likely contributes to some of the inconsistency in risk calculator estimation. It also limits the use of risk calculators in estimating the benefits of therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".