Working Towards Eye Health Equity for Indigenous Australians with Diabetes
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) poses significant challenges to individuals and broader society, much of which is borne by disadvantaged and marginalised population groups including Indigenous people. The increasing prevalence of T2DM among Indigenous people has meant that rates of diabetes-related complications such as blindness from end-stage diabetic retinopathy (DR) continue to be important health concerns. Australia, a high-income and resource-rich country, continues to struggle to adequately respond to the health needs of its Indigenous people living with T2DM. Trends among Indigenous Australians highlight that the prevalence of DR has almost doubled over two decades, and the prevalence of diabetes-related vision impairment is consistently reported to be higher among Indigenous Australians (5.2%-26.5%) compared to non-Indigenous Australians (1.7%). While Australia has collated reliable estimates of the eye health burden owing to T2DM in its Indigenous population, there is fragmentation of existing data and limited knowledge on the underlying risk factors. Taking a systems approach that investigates the social, environmental, clinical, biological and genetic risk factors, and-importantly-integrates these data, may give valuable insights into the most important determinants contributing to the development of diabetes-related blindness. This knowledge is a crucial initial step to reducing the human and societal impacts of blindness on Indigenous Australians, other priority populations and society at large.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 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".