Awareness Among Primary Care Physicians in Canada of Guideline Recommendations for Lowering LDL-Cholesterol in High-Risk Patients
Bibliographic record
Abstract
Background: Cardiovascular (CV) risk management for high-risk patients is often provided by primary care physicians (PCPs). We surveyed Canadian PCPs regarding their awareness and implementation of the 2021 Canadian Cardiovascular Society (CCS) lipid guideline recommendations for patients following an acute coronary syndrome (ACS) and those with diabetes but without CV disease. Methods: A committee of PCPs and specialists with lipid expertise, including some 2021 CCS lipid guideline coauthors, designed a survey to probe PCP awareness and practice patterns regarding CV risk management. From a national database, a total of 250 PCPs completed the survey between January and April 2022. Results: Almost all PCPs (97.2%) concurred that a post-ACS patient should be seen by their PCP within 4 weeks of hospital discharge (81.2% said within 2 weeks). Almost half (44.4%) responded that discharge summaries provided inadequate information, and 41.6% felt that lipid management post-ACS was the responsibility primarily of specialists. A total of 58.4% articulated that they face challenges when seeing a post-ACS patient, related to inadequate discharge information, complexities of polypharmacy and duration of therapies, and managing statin intolerance. A total of 63.2% and 43.6% correctly identified low-density lipoprotein cholesterol (LDL-C) intensification thresholds of 1.8 mmol/L in post-ACS patients, and 2.0 mmol/L in diabetes patients, respectively, and 81.2% incorrectly thought that proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors were indicated for patients with diabetes but without CV disease. Conclusions: One year following publication of the 2021 CCS lipid guidelines, our survey reveals knowledge gaps among responding PCPs regarding intensification thresholds and treatment options for patients post-ACS, or those with diabetes. Innovative and effective knowledge-translation programs to address these gaps are desirable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".