Indigenous health: update on the impact of diabetes and chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: With respect to chronic diseases such as diabetes and its complications, indigenous populations are known to suffer from poor health outcomes in comparison with whites. The purpose of this review is to highlight recent epidemiologic and intervention studies that have occurred in the areas of diabetes and renal disease among indigenous populations. RECENT FINDINGS: The burden of diabetes is increasing among younger indigenous groups with epidemic levels of end-stage kidney disease. As dialysis therapy has contributed to prolong life among indigenous patients, cardiovascular disease has now become the leading cause of mortality in these populations. Clear preventive intervention strategies to improve rates of progression to end-stage kidney disease are not prevalent nor are they emerging over time. Access to kidney transplantation is also reduced among indigenous populations in Australia, New Zealand, the USA and Canada. Reasons for this disparity are unclear but likely multifactorial. SUMMARY: Diabetes and its complications have produced a health crisis among indigenous populations. The impact on healthcare systems in countries where these indigenous populations reside will be substantial unless significant efforts are made to improve diabetic renal disease outcomes in the near future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".