Diabetes in south Asians: etiology and the complexities of care
Bibliographic record
Abstract
Fifteen to twenty percent of south Asians will develop type 2 diabetes mellitus. This extremely high prevalence of diabetes is seen in both south Asians living in developed countries and in south Asians who are living in either urban or rural south Asia. South Asians have specific diabetic risk factors resulting from a tendency to develop metabolically active abdominal fat, resulting in a poor lipid profile even at a low body mass index. They are also particularly vulnerable to microvascular and macrovascular diabetic complications including renal and cardiac disease. The increased prevalence of diabetes in south Asians is likely due to a combination of biological and cultural factors. Targeting these factors is the only way to provide effective education, prevention, screening, and treatment to south Asians. Culturally focused community programs and interprofessional care teams are two health care paradigms that have been successful in helping them manage this chronic illness. Continuing culturally targeted care and education programs is necessary to reduce the prevalence and complications of diabetes in south Asian communities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".