Does Management of Lipid Lowering Differ Between Specialists and Primary Care: Insights from GOAL Canada.
Bibliographic record
Abstract
Background: We studied whether significant differences in care gaps exist between specialists and PCPs. Methods: GOAL Canada enrolled patients with CVD or familial hypercholesterolemia (FH) and LDL-C > 2.0 mmol/L despite maximally tolerated statin therapy. During follow-up, physicians received online reminders of treatment recommendations based on Canadian Guidelines. Results: A total of 177 physicians (58% PCPs) enrolled 2009 patients; approximately half of the patients were enrolled by each physician group. Patients enrolled by specialists were slightly older (mean age 63 years vs. 62), female (45% vs. 40%), Caucasian (77% vs. 65%), and had a slightly higher systolic pressure and lower heart rate. Patients enrolled by specialists had less frequent history of familial hypercholesterolemia, diabetes, hypertension, chronic kidney disease and liver disease but more frequent history of coronary artery disease, atrial fibrillation and premature family history of CVD. There was no significant baseline difference in LDL-C, HDL-C, or non-HDL-c, although total cholesterol and triglycerides were slightly higher in patients managed by PCPs. At baseline, PCPs were more likely to use statins (80% vs.73%, p=0.0002) and other therapies such as niacin or fibrate (10% vs. 6%, p=0.0006) but similar use of ezetimibe (24% vs. 27%, p=0.15). At the end of follow up, specialists used less statins (70% vs. 77%, p=0.0005) and other therapies (6% vs. 10%, p=0.007) but more ezetimibe (45% vs. 38%, p=0.01) and the same frequency of PCSK9i (28% vs. 27%, p=0.65). The proportion of patients achieving the recommended LDL-C level of 2.0 mmol/L or below (primary endpoint) was similar at last available visit between specialists and PCPs (44% vs. 42%, p=0.32). Conclusion: Despite minor differences in the clinical profile of their patients, both PCPs and specialists actively participate in the management of lipid lowering therapy in high risk CVD patients and experience similar challenges and care gaps.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".