Ethnicity and Sex Affect Diabetes Incidence and Outcomes
Bibliographic record
Abstract
OBJECTIVE: Diabetes guidelines recommend aggressive screening for type 2 diabetes in Asian patients because they are considered to have a higher risk of developing diabetes and potentially worse prognosis. We determined incidence of diabetes and risk of death or macrovascular complications by sex among major Asian subgroups, South Asian and Chinese, and white patients with newly diagnosed diabetes. RESEARCH DESIGN AND METHODS: Using population-based administrative data from British Columbia and Alberta, Canada (1997-1998 to 2006-2007), we identified patients with newly diagnosed diabetes aged ≥35 years and followed them for up to 10 years for death, acute myocardial infarction, stroke, or hospitalization for heart failure. Ethnicity was determined using validated surname algorithms. RESULTS: There were 15,066 South Asian, 17,754 Chinese, and 244,017 white patients with newly diagnosed diabetes. Chinese women and men had the lowest incidence of diabetes relative to that of white or South Asian patients, who had the highest incidence. Mortality in those with newly diagnosed diabetes was lower in South Asian (hazard ratio 0.69 [95% CI 0.62-0.76], P < 0.001) and Chinese patients (0.69 [0.63-0.74], P < 0.001) then in white patients. Risk of acute myocardial infarction, stroke, or heart failure was similar or lower in the ethnic groups relative to that of white patients and varied by sex. CONCLUSIONS: The incidence of diagnosed diabetes varies significantly among ethnic groups. Mortality was substantially lower in South Asian and Chinese patients with newly diagnosed diabetes than in white patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".