Cardiovascular risk factors in Croatia: struggling to provide the evidence for developing policy recommendations
Bibliographic record
Abstract
Reliable epidemiological data on cardiovascular risk factors in Croatia have been lacking. This new study identifies targets for interventions Cardiovascular disease is the major cause of death in most European transitional countries.1 Among these countries, standardised mortality from cardiovascular disease is highest in Hungary (508 per 100 000 population) and Croatia (500/100 000) and lowest in Slovenia (295/100 000) and central European countries (238/100 000). In Croatia, cardiovascular disease is the leading cause of death and accounts for more than half the overall mortality.1 Furthermore, cardiovascular mortality has been constantly rising since the 1970s. Until recently, no reliable epidemiological data were available on the prevalence of cardiovascular risk factors in the Croatian population. The existing studies only comprised small unrepresentative samples and provided conflicting results. Hence, there was no evidence base for developing policy on reducing the burden of cardiovascular disease in the future and recommending interventions for people with cardiovascular risk factors.2 3 In collaboration with the Canadian Society for International Health, we conducted the Croatian adult health survey in the summer of 2003 among citizens aged 18 and older. After we stratified the country by region (as defined by the Croatian Central Bureau of Statistics), the sample comprised 10 766 randomly selected households; 9070 individuals agreed to participate (overall response rate 84.2%). This was the first representative population survey to be conducted in Croatia.
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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 | 0.087 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".