Risk Trajectories of Type 2 Diabetes–Related Microvascular Complications Across Racial/Ethnic Groups Among Older Adults
Bibliographic record
Abstract
INTRODUCTION: Type 2 diabetes mellitus (T2DM) microvascular complications are a major public health issue that disproportionately affects racial/ethnic minorities in the US. We aimed to address the limited understanding of racial/ethnic disparities in the longitudinal natural history of microvascular complications over eight years among older adults with T2DM in the US and Canada. METHODS: From 10,251 participants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) (2003-2009) trial, we derived 6323 participants. We examined racial/ethnic disparities of composite microvascular complications, nephropathy, neuropathy, and retinopathy outcomes among adults of Black (n = 1099), White (n = 4071), and Other races (n = 1156), over 8 years. We used linear mixed-effects model with random intercept to account for intra-person correlation. RESULTS: Our linear mixed-effects models displayed that the odds of composite microvascular complications for other race, which primarily consisted of Hispanic adults declined over a period of time [OR: 0.95 (0.93-0.99)]. The odds increased over time for Black [1.06 (1.03-1.09)] and White [1.11 (1.09-1.13)] adults (P < 0.001). CONCLUSION: Other race adults had a declining risk of microvascular complications over time. This decline was not observed among Black and White adults.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".