Geographical variations in the prevalence and management of cardiovascular risk factors in outpatients with CAD: Data from the contemporary CLARIFY registry
Bibliographic record
Abstract
AIM: To determine the current prevalence and control of major cardiovascular risk factors in stable CAD outpatients worldwide. METHODS: We analysed variations in cardiovascular risk factors in stable CAD outpatients from CLARIFY, a 5-year observational longitudinal cohort study, in seven geographical zones (Western/Central Europe; Canada/South Africa/Australia/UK; Eastern Europe; Central/South America; Middle East; East Asia; and India). RESULTS: Patient presentation (N=32,954, mean age 64.2 years, 78% male) varied between zones, as did prevalence of risk factors (all p < 0.0001). Obesity ranged from 20% (East Asia) to 42% (Middle East), raised blood pressure from 28% (Central/South America and East Asia) to 48% (Eastern Europe), raised LDL cholesterol from 24% (Canada/South Africa/Australia/UK) to 65% (Eastern Europe), elevated heart rate (≥70 bpm) from 38% (Western/Central Europe) to 78% (India), diabetes from 17% (Eastern Europe) to 60% (Middle East), and smoking from 6% (Central/South America) to 19% (Eastern Europe). Aspirin and lipid-lowering drugs were widely used everywhere (≥84% and ≥88%, respectively). Rates of risk factor control varied geographically (all p < 0.0001). Rate of controlled blood pressure in hypertension varied from 47% (Eastern Europe) to 66% (Central/South America), glucose control in diabetes from 23% (India) to 51% (Western/Central Europe and East Asia), controlled LDL cholesterol and dyslipidaemia from 32% (Eastern Europe) to 75% (Canada/South Africa/Australia/UK), heart rate <70 bpm from 22% (India) to 62% (Western/Central Europe), and heart rate ≤60 bpm in angina patients from 2% (India) to 29% (Canada/South Africa/Australia/UK and Central/South America). CONCLUSION: Prevalence and control of major cardiovascular risk factors in stable CAD vary markedly worldwide. Many stable CAD outpatients are being treated suboptimally.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".