Quality of cardiovascular disease care in Ontario, Canada: missed opportunities for prevention - a cross sectional study
Bibliographic record
Abstract
BACKGROUND: Primary care plays a key role in the prevention and management of cardiovascular disease (CVD). We examined primary care practice adherence to recommended care guidelines associated with the prevention and management of CVD for high risk patients. METHODS: We conducted a secondary analysis of cross-sectional baseline data collected from 84 primary care practices participating in a large quality improvement initiative in Eastern Ontario from 2008 to 2010. We collected medical chart data from 4,931 patients who either had, or were at high risk of developing CVD to study adherence rates to recommended guidelines for CVD care and to examine the proportion of patients at target for clinical markers such as blood pressure, lipid levels and hemoglobin A1c. RESULTS: Adherence to preventive care recommendations was poor. Less than 10% of high risk patients received a waistline measurement, half of the smokers received cessation advice, and 7.7% were referred to a smoking cessation program. Gaps in care exist for diabetes and kidney disease as 54.9% of patients with diabetes received recommended hemoglobin-A1c screenings, and only 55.8% received an albumin excretion test. Adherence rates to recommended guidelines for coronary artery disease, hypertension, and dyslipidemia were high (>75%); however <50% of patients were at target for blood pressure or LDL-cholesterol levels (37.1% and 49.7% respectively), and only 59.3% of patients with diabetes were at target for hemoglobin-A1c. CONCLUSIONS: There remain significant opportunities for primary care providers to engage high risk patients in prevention activities such as weight management and smoking cessation. Despite high adherence rates for hypertension, dyslipidemia, and coronary artery disease, a significant proportion of patients failed to meet treatment targets, highlighting the complexity of caring for people with multiple chronic conditions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".