A Comparison of Rates, Risk Factors, and Outcomes of Gestational Diabetes Between Aboriginal and Non-Aboriginal Women in the Saskatoon Health District
Bibliographic record
Abstract
OBJECTIVE: To determine possible differences in gestational diabetes mellitus (GDM) between aboriginal and non-aboriginal people in the Saskatoon Health District. RESEARCH DESIGN AND METHODS: This was a prospective survey of all women admitted for childbirth to the Saskatoon Royal University Hospital between January and July 1998. We compared prevalence rates, risk factors, and outcomes of GDM between aboriginal and non-aboriginal women. RESULTS: Information was obtained from 2,006 women, of whom 252 aboriginal and 1,360 non-aboriginal subjects had been tested for GDM. The overall rates of GDM were 3.5% for women in the general population and 11.5% for aboriginal women. For those living within the Saskatoon Health District, GDM rates were 3.7 and 6.4%, respectively. Multivariate analysis demonstrated that aboriginal ethnicity, most notably when combined with obesity, was an independent predictor for GDM. Pregravid BMI > or = 27 kg/m(2) and maternal age > or = 33 years were the most important risk factors for GDM in aboriginal women, whereas previous GDM, family history of diabetes, and maternal age > or = 38 years were the strongest predictors for GDM in non-aboriginal women. CONCLUSIONS: There may be fundamental differences in GDM between aboriginal and non-aboriginal people. Because GDM contributes to an increased risk for type 2 diabetes in aboriginal women and their offspring, the impact of prevention and optimal treatment of GDM on the type 2 diabetes epidemic in susceptible populations are important areas for further investigation.
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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 | 0.000 |
| 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".