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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".