GOAL Canada: Physician Education and Support Can Improve Patient Management
Bibliographic record
Abstract
Background Despite the widespread use of statins, approximately 40% to 50% of Canadian patients with known cardiovascular disease do not achieve the low-density lipoprotein cholesterol (LDL-C) goal. G uidelines O riented A pproach to L ipid lowering (GOAL) is an investigator-initiated study aiming to ascertain the use of second- and third-line therapy and its impact on LDL-C goal achievement in a real-world setting. Methods GOAL enrolled patients with clinical vascular disease or familial hypercholesterolemia and LDL-C > 2.0 mmol/L despite maximally tolerated statin therapy. During follow-up, physicians managed patients as clinically indicated but with online reminders of guideline recommendations. Results Of 2009 patients enrolled (median age 63 years, 42% were female), baseline total cholesterol was 5.5 ± 1.4 mmol/L, LDL-C was 3.3 ± 1.3 mmol/L, non–high-density lipoprotein cholesterol was 4.1 ± 1.4 mmol/L, high-density lipoprotein cholesterol was 1.3 ± 0.4 mmol/L, and triglycerides were 2.0 ± 1.5 mmol/L. Lipid-lowering therapy used at baseline was statin therapy in 76% (with 24% statin intolerant) and ezetimibe in 25%. During follow-up, the proportion of patients achieving an LDL-C level of < 2.0 mmol/L increased significantly to 50.8% as a result of additional lipid-lowering therapy. Patients achieving the recommended LDL-C level were more likely to not be statin intolerant (83.8% vs 70.7%, P < 0.0001) and to be taking a high-efficacy type and dose of statin (52.4% vs 35.9%, P < 0.0001). The 3 top reasons for not using the recommended therapy with ezetimibe were patient refusal in 33%, not needed in 22%, and intolerance in 20%, whereas for PCSK9i the reasons were cost in 26%, not needed in 27%, or patient refusal in 25%. Conclusion The results indicate the feasibility of optimizing management, resulting in achievement of the guideline-recommended LDL-C level. This has the potential to translate into reductions in cardiovascular morbidity and mortality of Canadian patients.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".