Documenting Cardiovascular-Kidney-Metabolic Risk and Disease Within an Aboriginal Cohort
Bibliographic record
Abstract
AIM: Aboriginal and Torres Strait Islander people experience high burden of cardiovascular, kidney and metabolic conditions, often manifesting in multimorbidity and contributing to over one third of life expectancy differentials. This article explores cardiovascular-kidney-metabolic (CKM) health within an Aboriginal cohort by documenting the burden of early risk, disease and factors associated with disease progression. METHODS: A prospective longitudinal cohort of 601 Aboriginal people living in Central Australia spanning 2008-2016 was utilised. Research was driven by and based on community priorities and partnerships. Baseline data included questionnaires, clinical assessments and primary health care data; follow-up outcomes were derived from primary care clinical review, administrative hospitalisation and mortality datasets. RESULTS: Four percent of participants (mean: 41.3 years; 47% female) had no CKM risk factors (Stage 1 CKM Syndrome or greater) at baseline, 54.6% had established cardiovascular disease, chronic kidney disease, and/or diabetes mellitus. Greater presence of CKM disease and co/multimorbidity was associated with greater socioeconomic disadvantage. After adjusting for age, participants with co/multimorbidity were more likely to die during follow-up (hazard ratio [95% confidence interval]: 2.2 [1.1-4.3]) than participants without clinical disease at baseline. During a mean follow-up period of 6.8 years, 30.4% of participants living with no clinical disease at baseline developed at least one CKM condition, and 25% progressed to co/multimorbidity. CONCLUSIONS: This study reveals a higher prevalence of cardiovascular, kidney, and metabolic risk and disease than previously reported and compared to non-Indigenous counterparts. The health sector must recalibrate disease prevention, move beyond single-organ management and implement interdisciplinary care coordination to prevent expansion of inequities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".