The Local Burden of Disease for Non-Rheumatic-Acquired Valvular Heart disease in Iran, North Africa and Middle East from 1990 to 2017: Findings from a Sub-Analysis of the Global Burden of Disease Study 2017
Bibliographic record
Abstract
Abstract Background: Cardiovascular diseases (CVDs) are one of the main causes of mortality and a major barrier to sustainable development, being the main cause of “Disability-Adjusted Life Years” (DALYs), “Years of Life lost” (YLLs) and “Years Lived with Disability” (YLDs). Among CVDs, one of the most common and treatable CVD is non-rheumatic-acquired valvular heart disease (NRVD). However, in some countries such as Iran, the burden of NRVD is almost unknown because previous studies have focused mostly on the burden of ischemic heart disease, heart failure and other cardiovascular diseases. Method: Using data from the 2017 “Global Burden of Disease study”, we compared the number of deaths, DALYs, YLLs, YLDs, incidence and prevalence trends for NRVD in Iran, North Africa and Middle East since 1990 to 2017. Results: Our study yielded 3 major results: 1) a higher rate of death in Iran compared with North Africa, the Middle East and globally; 2) a higher increase in DALYs, YLLs, and YLDs in Iran in comparison with North Africa, the Middle East, and globally from 1990 to 2017; and 3) vast differences in increasing rates of prevalence and incidence of NRVD in Iran compared with the global trends from 1990 to 2017. The significant difference in the prevalence rate of NRVD in Iran versus global rate can be due to the higher growth rates of aging and the co-morbidities associated with NRVD, such as hypertension. Conclusion: Iranian health policy- and decision-makers should allocate significant resources for their diagnosis, treatment and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".