The prevalence of cardiovascular risk factors and cardiovascular disease among primary care patients in Poland: results from the LIPIDOGRAM2015 study
Bibliographic record
Abstract
BACKGROUND AND AIM: To estimate the prevalence of cardiovascular (CV) disease and CV risk factors among Polish patients. METHODS: A nationwide cross-sectional study, LIPIDOGRAM2015, was carried out in Poland in the 4th quarter of 2015 and 1st and 2nd quarters of 2016; 438 primary care physicians enrolled 13,724 adult patients that sought medical care in primary health care practices. RESULTS: Nearly 19% of men and approximately 12% of women had cardiovascular disease (CVD). Over 60% of the recruited patients had hypertension (HTN), >80% had dyslipidaemia and <15% of patients were diagnosed with diabetes (DM). All of these disorders were more frequent in men. In 80% of patients the waist circumference exceed norm for the European population. Less than half of the patients were current smokers or had smoked in the past. Patients with CVD had significantly higher blood pressure and glucose levels but lower low density lipoprotein-cholesterol level. CONCLUSIONS: The prevalence of CVD and CV risk factors among patients in Poland is high. CVD is more common in men than in women. The most common CV risk factors are excess waist circumference, dyslipidaemia and HTN. Family physicians should conduct activities to prevent, diagnose early and treat CVD in the primary health care population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".