The Global Burden and Risk Factors of Cardiovascular Diseases in Adolescent and Young Adults: A Systematic Review
Bibliographic record
Abstract
Background and Objectives: Globally, cardiovascular diseases (CVDs), mainly stroke and ischemic heart disease (IHD) remain the leading and major causes of mortality in addition to being the key contributors to disabilities. The objective of this systematic review entails the evaluation of the global burden and the risk factors associated with CVDs in adolescents and young adults. To attain this objective, the study will examine the various underlying causes of CVD mortality and the associated risk factors. Methodology: The study entailed an in-depth search of various online databases for original studies focusing on the global burden of CVDs and risk factors in adolescents and young adults. The search was conducted on databases that included Embase, PubMed, Google Scholar, SCOPUS, and Web of Science. The identified studies were subjected to evaluation and screening, and the selection of the apt studies was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 15 studies were selected and included in this systematic review Results: CVD prevalence and incidence rates in adolescents and young adults are highest in low and low-middle socio-demographic index (SDI) nations, despite the CVD burden progressively increasing in high and high-middle SDI countries. Male adolescents and young adults have the highest prevalence, incidence, and disability-adjusted life year (DALY), and mortality rates for endocarditis, even as females aged 30 to 39 years have the highest atrial fibrillation and atrial flutter-related DALY and mortality rates. Conclusion: The global CVD burden in adolescents and young adults remains a major global health challenge. Therefore, it is important that factors that include disparities observed in the SDI levels amongst the nations, age and gender attributes of the populaces, the primary CVD types, and the various attributable risk factors are taken into consideration during the formulation and execution of prevention strategies and interventions.
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.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".