Type 2 diabetes and health care costs in Latin America: exploring the need for greater preventive medicine
Bibliographic record
Abstract
BACKGROUND: Despite advances in medicine, health systems in Latin America are not coping with the challenges of chronic diseases. Incidence of disease and the economic burdens as a consequence have both increased in recent years. We have chosen Type 2 diabetes as an example to highlight the challenges posed by chronic diseases, in terms of the epidemiological transition and the economic burden of the demand for services to treat such problems. DISCUSSION: Current health systems are not prepared to respond in a comprehensive manner to all phases of the natural history of the disease. There are new models of universal coverage, but resources and models of care are focused on programs aimed at healing/rehabilitation, and very sparsely at detection/prevention. SUMMARY: In this scenario, chronic problems have alarmingly increased direct costs (medical care) and indirect costs (temporary disability, permanent disability and premature mortality). If more resources are not assigned to preventive medicine, these trends, in addition to not meeting the needs of the population, will financially collapse health systems and the patients' pockets. This Opinion piece outlines some possible changes that can be implemented to better prepare the health services in Latin American countries.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".