Primary prevention of cardiovascular disease: global cardiovascular risk assessment and management in clinical practice
Bibliographic record
Abstract
AIMS: For the primary prevention of cardiovascular disease, the Framingham Risk Score (FRS) is the most well-known risk prediction method. However, there are limited data regarding physicians' method of risk assessment and guideline adherence in clinical practice. METHODS AND RESULTS: In the PARADIGM (Primary cARe AuDIt of Global risk Management) study (March 2009-10), 105 primary care physicians across Canada prospectively collected data for 3015 patients (mean age 56 years, 59% men) without known cardiovascular disease, diabetes, or lipid-lowering medications at baseline. For each patient, the treating physician determined their cardiovascular risk, and reported the risk stratification method and subsequent treatment decisions. Kappa statistics assessed the agreement between the study-calculated FRS and the treating physician's reported risk assessment. The FRS was the most commonly reported risk assessment method, but was used in only 34.0% of patients. Regardless of the method used (even if the FRS was reportedly used), there was only fair agreement between the risk stratification as reported by the physician and the study-calculated FRS. Moreover, physicians recommended statin initiation in 92% of all patients that they identified as high risk; however, according to the study-calculated FRS, only 56% of the truly high-risk patients were recommended statin therapy. CONCLUSION: For the primary prevention of cardiovascular disease, these findings indicate a need to improve risk assessment and stratification, as misclassification directly contributes to suboptimal risk factor management in real-world clinical practice. Future studies should establish the optimal risk stratification method with quality improvement strategies for its subsequent implementation. CLINICAL TRIAL REGISTRATION: http://clinicaltrials.gov/ct2/show/NCT00950703; NCT00950703.
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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 | 0.042 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".