Challenges in management and prevention of ischemic heart disease in low socioeconomic status people in LLMICs
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases, principally ischemic heart disease (IHD), are the most important cause of death and disability in the majority of low- and lower-middle-income countries (LLMICs). In these countries, IHD mortality rates are significantly greater in individuals of a low socioeconomic status (SES). MAIN TEXT: Three important focus areas for decreasing IHD mortality among those of low SES in LLMICs are (1) acute coronary care; (2) cardiac rehabilitation and secondary prevention; and (3) primary prevention. Greater mortality in low SES patients with acute coronary syndrome is due to lack of awareness of symptoms in patients and primary care physicians, delay in reaching healthcare facilities, non-availability of thrombolysis and coronary revascularization, and the non-affordability of expensive medicines (statins, dual anti-platelets, renin-angiotensin system blockers). Facilities for rapid diagnosis and accessible and affordable long-term care at secondary and tertiary care hospitals for IHD care are needed. A strong focus on the social determinants of health (low education, poverty, working and living conditions), greater healthcare financing, and efficient primary care is required. The quality of primary prevention needs to be improved with initiatives to eliminate tobacco and trans-fats and to reduce the consumption of alcohol, refined carbohydrates, and salt along with the promotion of healthy foods and physical activity. Efficient primary care with a focus on management of blood pressure, lipids and diabetes is needed. Task sharing with community health workers, electronic decision support systems, and use of fixed-dose combinations of blood pressure-lowering drugs and statins can substantially reduce risk factors and potentially lead to large reductions in IHD. Finally, training of physicians, nurses, and health workers in IHD prevention should be strengthened. CONCLUSION: The management and prevention of IHD in individuals with a low SES in LLMICs are poor. Greater availability, access, and affordability for acute coronary syndrome management and secondary prevention are important. Primary prevention should focus on tackling the social determinants of health as well as policy and individual interventions for risk factor control, supported by task sharing and use of technology.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".