Patient Knowledge of Coronary Risk Profile Improves the Effectiveness of Dyslipidemia Therapy
Bibliographic record
Abstract
BACKGROUND: Despite increasing evidence that treating dyslipidemia reduces cardiovascular events, many patients do not achieve recommended lipid targets. METHODS: To determine whether showing physicians and patients the patient's calculated coronary risk can improve the effectiveness of treating dyslipidemia in a primary care setting, patients were randomized to receive usual care or ongoing feedback regarding their calculated coronary risk and the change in this risk after lifestyle changes, pharmacotherapy, or both to treat dyslipidemia. Outcomes, based on intention-to-treat analysis, included changes in blood lipid levels, coronary risk, and the frequency of reaching lipid targets. RESULTS: Two hundred thirty primary care physicians enrolled 3,053 patients. After 12 months of follow-up, 2,687 patients (88.0%) remained in the study. After adjustment for baseline lipid values, significantly greater mean reductions in low-density lipoprotein cholesterol levels and the total cholesterol to high-density lipoprotein cholesterol ratio were observed in patients receiving risk profiles (51.2 mg/dL [to convert to millimoles per liter, multiply by 0.0259] and 1.5, respectively) vs usual care (48.0 mg/dL and 1.3, respectively), but the differences were small (-3.3 mg/dL; 95% confidence interval [CI], -5.4 to -1.1 mg/dL; and -0.1; 95% CI, -0.2 to -0.1, respectively). Patients in the risk profile group were also more likely to reach lipid targets (odds ratio, 1.26; 95% CI, 1.07 to 1.48). A significant dose-response effect was also noted when the impact of the risk profile was stronger in those with worse profiles. CONCLUSIONS: Discussing coronary risk with the patient is associated with a small but measurable improvement in the efficacy of lipid therapy. The value of incorporating risk assessment in preventive care should be further evaluated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".