Risk assessment of atherosclerotic cardiovascular diseases before statin therapy initiation: Knowledge, attitude, and practice of physicians in Yemen
Bibliographic record
Abstract
BACKGROUND: Risk evaluation of atherosclerotic cardiovascular disease (ASCVD) remains the cornerstone of primary prevention. The cardiovascular risk assessment can guide the decision-making on various preventive measures such as initiating or deferring statin therapy. Thus, our study aimed to assess the physicians' knowledge, attitude, and practices regarding atherosclerotic cardiovascular diseases risk assessment. Also, we evaluated the physician-patient discussion and counseling practices before statin therapy initiation in concordance with recommendations from the latest clinical practice guideline. METHODS: A cross-sectional study was conducted between November 2020 and January 2021. A self-administered questionnaire was distributed to 350 physicians (GPs, residents, specialists, and consultants). Two trained pharmacists distributed the questionnaires in 5 major tertiary governmental hospitals and more than ten private hospitals. Also, private clinics were targeted so that we get a representative sample of physicians at different workplaces. RESULTS: A total of 270 physicians filled the questionnaire out of 350 physicians approached, with 14 being excluded due to high missing data, giving a final response rate of 73%. Participants had suboptimal knowledge and practices with a high positive attitude toward atherosclerotic cardiovascular diseases risk assessment. The knowledge and practices were higher among consultants, participants from the cardiology department, those with experience years of more than nine years, and those who reported following a specific guideline for cholesterol management or using a risk calculator in their practice. Notably, the risk assessment and counseling practices were lower among physicians who reported seeing more patients per day. CONCLUSION: Physicians had overall low knowledge, suboptimal practices, and a high positive attitude toward cardiovascular risk assessment. Therefore, physicians' training and continuing medical education regarding cholesterol management and primary prevention clinical practice guidelines are recommended. Also, the importance of adherence to clinical practice guidelines and their impact on clinical outcomes should be emphasized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".