POSTER SESSION 1: Thursday, 1 May 2008, 9:00–12:30 Location: Poster Area
Bibliographic record
Abstract
The global risk assessment became an important tool in the prevention strategy of cardiovascular diseases (CVD). According to the recent guidelines, a correction of the risk equations in relation to countryspecific mortality or risk profile is recommended. Bulgaria is a country among those with highest CVD mortality and morbidity. Purpose: To correct the SCORE formula for high risk populations in Europe for the Bulgarian female population and to apply it to a sample of healthy Bulgarian women. Methods: The 2001 gender-specific cardiovascular mortality in Bulgaria was used as baseline hazard in the original SCORE formula for high risk populations. The country specific mortality data in five-year age periods was logarithmically transformed and then linear regression was applied on thus transformed data. The original variables coefficients in SCORE were used. A graphical version was created. The modified formula was applied to a sample of 393 women from two large cities of Bulgaria without evidence of CVD. The correlation coefficient estimation, as well as Cohen's kappa analysis was used to compare the original and modified SCORE tables and ROC curve was created. Results: The mean age of the sample used for the validation was 57.87.3 years, range 40--69. The modified SCORE table showed that the Bulgarian women reach the higher risk levels approximately 2.5 years earlier than their European counterparts and the level of their global CV risk is generally higher. The median global CV risk of the sample rises from 2.0% to 2.95% after adjustment. The modified and the original formula correlate excellently with Spearman's r=0.969, p<0.0001 and AUC=0.88, 95% CI=0.85--0.94 (?<0.0001). Only in 2.3% of the participants the two ways of calculating the CV risk totally disagree. Conclusion: The Bulgarian women have higher levels of global risk for CVD and reach them approximately 2.5 years earlier in their life than the women in other European high-risk countries. Although there is a good agreement between the corrected and the original equation, there is an obvious need for modification of the SCORE model for the Bulgarian population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".