Adherence to Cardiovascular Prevention Guidelines in an Academic Centre
Bibliographic record
Abstract
Background: Adherence to guidelines is associated with better patient outcomes. Although studies show suboptimal adherence to cardiovascular prevention guidelines among general practitioners, adherence among specialist physicians is understudied. The aim of this analysis was to identify practice gaps among cardiologists in a tertiary academic centre. Methods: We retrospectively audited cardiology outpatient clinic notes taken at the Cardiology Clinic at the Centre hospitalier de l'Université de Montréal (CHUM), from the period January 1, 2019 to February 28, 2019. Data were abstracted from hospital medical records. The primary outcome of interest was the rate of adherence to cardiovascular prevention guidelines. We compared the chart-documented practice at our centre to the Canadian hypertension, lipid, diabetes, antiplatelet, and heart failure guidelines in effect at the time of the audit. We also collected information regarding discussions of smoking, alcohol consumption, physical activity, and diet. Results: A total of 2503 patients were included, with a mean age of 65.6 ± 14.5 years. Dyslipidemia occurred in 63% of patients, hypertension in 55%, and coronary artery disease in 41%. Optimal low-density lipoprotein control was documented as having been achieved in just 39% of cases. Blood pressure control was adequate for 65% of patients, and glycemic control was achieved in 47% of patients with diabetes. Heart failure treatment was optimal in 34% of patients. Nearly all patients with coronary artery disease (95%) had appropriate antithrombotic therapy. The incidence of discussion of nonpharmacologic interventions varied, ranging from 91% (smoking) to 16% (diet). Conclusions: Primary and secondary prevention of cardiovascular events was found to be suboptimal in an academic tertiary-care outpatient cardiology clinic and may be representative of similar shortcomings nationwide. Strategies to ensure guideline adherence are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".