Does First Nations ancestry modify the association between gestational diabetes and subsequent diabetes: a historical prospective cohort study among women in Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: Over the past 30 years, the prevalence of diabetes has steadily increased among Canadians, and is particularly evident among First Nations (FN) women. The interplay between FN ancestry, gestational diabetes and the development of subsequent diabetes among mothers remains unclear. METHODS: After excluding known pre-existing diabetes, we explored whether FN ancestry may modify the association between gestational diabetes and post-partum diabetes among women in Manitoba (1981-2011) via a historical prospective cohort database study. We analysed administrative data in the Population Health Research Data Repository using Kaplan-Meier survival analysis and Cox proportional hazards regression. RESULTS: Gestational diabetes was diagnosed in 11 906 of 404 736 deliveries (2.9%), 6.7% of FN and 2.2% of non-FN pregnant women (P < 0.0001). Post-partum diabetes during ≤ 30 years follow-up was more than three times higher among FN women than among non-FN women (P < 0.0001). Diabetes developed in 76.0% of FN and 56.2% of non-FN women with gestational diabetes within the follow-up period. The hazard ratio of gestational diabetes for post-partum diabetes was 10.6 among non-FN women and 5.4 among FN women. Other factors associated with a higher risk of diabetes included lower family income among FN and non-FN women and rural/remote residences among FN women. Among non-FN women, urban residence was associated with a higher risk of diabetes. CONCLUSION: Gestational diabetes increases post-partum diabetes in FN and non-FN women. FN women had substantially more gestational diabetes or post-partum diabetes than non-FN women, partially due to socio-economic and environmental barriers. Reductions in gestational diabetes and socio-economic inequalities are required to prevent diabetes in women, particularly in FN population.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".