Compliance of Secondary Prevention Strategies in Coronary Artery Disease Patients with and without Diabetes Mellitus – A Cross-Sectional Analytical Survey from Kerala, India
Bibliographic record
Abstract
Context: There is limited data related to compliance of secondary prevention strategies for coronary artery diseases (CAD) among patients with and without diabetes. Objectives: The objective was to compare compliance to secondary prevention strategies for CAD including smoking cessation, weight management, blood pressure (BP) control, Low density lipoprotein (LDL) cholesterol control and adequate physical activity between patients with and without diabetes. Settings and Design: This is a hospital-based cross-sectional analytical study. Methods and Materials: The study questionnaire was used to collect data through interviews of CAD patients. Compliance to secondary prevention strategies was documented using European Society of Cardiology guidelines. Statistical Analysis: We used modified Poisson model to estimate adjusted prevalence ratios (Adj. PR) for estimating compliance. Results: Among 1,206 participants with CAD, 609 (50.5%) had diabetes. The Adj. PR s for three targets – smoking cessation (Adj. PR 1.01, 95% CI 0.97, 1.06, P 0.50), ideal BMI (Adj. PR 0.99, 95% CI 0.92, 1.09, P 0.99) and adequate physical activity (Adj. PR 1.12, 95% CI 0.97, 1.29, P 0.12) showed no significant difference between the groups. There was poor BP control in patients with diabetes compared to those without the same (Adj. PR 0.19, 95% CI 0.15, 0.23, P < 0.0001). LDL cholesterol control was better in patients with diabetes in comparison to those without the same (Adj. PR 1.19, 95% CI 1.08, 1.31, P 0.0005). Conclusion: The compliance for secondary prevention of CAD among patients with diabetes is similar to those without diabetes except for poor control of hypertension and better control of LDL cholesterol.
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 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) | 0.001 | 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".