Comparison of Cardiovascular Risk Estimation and Statin Prescribing in Primary Care: A Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Statin prescribing for primary prevention remains a topic of debate, especially among individuals with low to moderate risk for cardiovascular disease (CVD), partly due to limitations of current cardiovascular risk assessment tools. This study aimed to determine whether differences exist in risk estimation among various cardiovascular risk calculators used in Canadian clinical practice guidelines and to describe the proportion of patients who may fall into a different risk category if an alternative risk calculator were used. METHODS: This work was approved by the local research ethics board. A retrospective chart review was conducted for adult patients aged 40 and older without a statin-indicated condition or prior cardiovascular event who underwent lipid assessment at a single family medicine center in London, Ontario, between 2010 and 2023. Three online calculators and two risk estimators (Framingham and American Society for Cardiovascular Disease) were used to re-estimate cardiovascular risk and compare the results with the values documented in the patient's chart. RESULTS: Of 50 patients, 20% did not have a documented cardiovascular risk value in their chart at the time of lipid assessment. However, the mean difference in Framingham risk values between the electronic medical record and the PEER (patients, experience, evidence, research) lipid online calculator was statistically significant (3.44%, 95% CI 0.11-6.76, p<0.05). Additionally, 23 (46%) patients would have fallen into a lower risk category according to Canadian clinical practice guidelines if the atherosclerotic cardiovascular disease (ASCVD) risk calculator had been used instead of the Framingham estimator for CVD risk assessment. CONCLUSION: Cardiovascular risk percentages differ between those calculated using an electronic medical record tool and those calculated with online calculators. Depending on the tool used, a proportion of patients may fall into a different cardiovascular risk category, resulting in different management decisions. Specifically, patients who would fall into a lower risk category could be considered for lifestyle management alone rather than statin initiation. Further research is needed to guide consistent use of available point-of-care risk tools by clinicians, and clinical practice guidelines should incorporate these recommendations.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".