Pharmacological treatment of hypertension and hyperlipidemia in Izhevsk, Russia
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in Russia. Hypertension and hyperlipidemia are important risk factors for CVD that are modifiable by pharmacological treatment and life-style changes. We aimed to characterize the extent of the problem in a typical Russian city by examining the prevalence, treatment and control rates of hypertension and hyperlipidemia and investigating whether the specific pharmacological regimes used were comparable with guidelines from a country with much lower CVD rates. METHODS: The Izhevsk Family Study II included a cross-sectional survey of a population sample of 1068 men, aged 25-60 years conducted in Izhevsk, Russia (2008-2009). Blood pressure and total cholesterol were measured and self-reported medication use was recorded by a clinician. We compared drug treatments with the Russian and Canadian treatment guidelines for hypertension and hyperlipidemia. RESULTS: The prevalence of hypertension was 61 % (age-standardised prevalence 51 %), with 66 % of those with hypertension aware of their diagnosis and 50 % of those aware taking treatment. 17 % of those taking treatment achieved blood pressure control. The majority (59 %) of those taking treatment were not doing so regularly. Prevalence of hyperlipidemia was 45 % (age-standardised prevalence 40 %), however less than 2 % of those with hyperlipidemia were taking any treatment. Types of lipid-lowering and anti-hypertensive medications prescribed were broadly in line with Russian and Canadian guidelines. CONCLUSION: The prevalence of hypertension and hyperlipidemia is high in Izhevsk while the proportion of those treated and attaining treatment targets is very low. Prescribed medications were concurrent with those in Canada, but adherence is a major issue.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".