The Inter- and Intragenerational Impact of Gestational Diabetes on the Epidemic of Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVES: We investigated the contribution of gestational diabetes mellitus (GDM) to the historic epidemic of type 2 diabetes mellitus (T2DM) in Saskatchewan. METHODS: We constructed a population-level simulation model of the inter- and intragenerational interaction of GDM and T2DM for the period 1956 to 2006. The model was stratified by gender, ethnicity, and age; parameterized with primary and secondary data; and calibrated to match historic time series. Risk of diabetes was sigmoidally trended to capture exogenous factors. RESULTS: Best-fit calibrations suggested GDM may be responsible for 19% to 30% of the cases of T2DM among Saskatchewan First Nations people, but only for approximately 6% of cases among other persons living in Saskatchewan. The estimated contribution of GDM to the growth in T2DM was highly sensitive to assumptions concerning the post-GDM risk of developing T2DM. CONCLUSIONS: GDM may be an important driver for the T2DM epidemic in many subpopulations. Because GDM is a readily identifiable, preventable, and treatable condition, investments in prevention, rapid diagnosis, and evidence-based treatment of GDM in at-risk populations may offer substantial benefit in lowering the T2DM burden over many generations. Model-informed data collection can aid in assessing intervention tradeoffs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".