Burden of Ischemic Heart Disease in Central Asian Countries, 1990–2017
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The burden of ischemic heart disease (IHD) is high. There is limited information on the burden of IHD in identified high risk areas like Central Asia (CA) which is comprised of Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Turkmenistan, Mongolia, Uzbekistan and Tajikistan. This study addresses the burden of IHD in CA at the regional and country levels. METHODS: Using data from the latest iteration of the Global Burden of Disease Study (GBD), this study provides age-adjusted mortality, prevalence, and Disability Adjusted Life Years (DALYs) of IHD by sex in the CA region, and national levels for countries in this region from 1990 to 2017. RESULTS: The CA region has a higher IHD burden than the rest of the world over the studied period. Amongst the countries within this region, age-standardized mortality and DALY rates in Uzbekistan are the highest not only in CA but worldwide, while Armenia consistently has the lowest IHD burden in CA. Unhealthy diet, high systolic blood pressure and LDL-cholesterol are the risk factors with the highest attributable IHD DALYs. CONCLUSION: There is considerable variation in IHD DALY rates among countries in the CA region. The reasons for such differences are likely multifactorial such as differences in risk factors distribution, health care effectiveness, political, social and economic factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it