The Burden of Diabetes in Argentina
Bibliographic record
Abstract
OBJECTIVE: To measure the economic burden of diabetes in Argentina by age, gender and region for the year 2005, in disability-adjusted life years (DALYs). METHODS: DALYs were estimated by the sum of years of life lost due to premature death (YLL) and years of life lived with disability (YLD). RESULTS: In the population studied (20 to 85 years), the burden of diabetes without complications was 1.3 million DALYs, 85% of which were caused by disabilities. Whereas mortality rates (YLL) increased as a function of age, YLD showed the opposite relationship. Women had higher burden of disease values, represented by 51 and 61% of YLL and YLD, respectively, independently of age. CONCLUSIONS: Our results demonstrate that disabilities are a key component of diabetes burden; its regular and systematic estimation would allow to design effective prevention strategies, to assess the impact of their implementation and to optimize resource allocation based on objective evidence.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".