Glucose Tolerance Status in Pregnancy: A Window to the Future Risk of Diabetes and Cardiovascular Disease in Young Women
Bibliographic record
Abstract
It has long been recognized that the diagnosis of gestational diabetes mellitus (GDM) identifies a population of young women at high risk of developing Type 2 diabetes (T2DM) in the future. In recent years, however, a series of studies have revealed that antepartum glucose tolerance screening, a standard element of current obstetrical care instituted for the purpose of detecting GDM, may provide previously-unrecognized insight into a woman's future risk of metabolic and vascular disease. Indeed, it has emerged that in fact any degree of abnormal glucose tolerance detected on antepartum screening (i.e. not just GDM) predicts an increased future risk of pre-diabetes and diabetes, one that is proportional to the severity of dysglycemia observed in pregnancy. In addition, in the years following the index pregnancy, women with a history of GDM exhibit an enhanced cardiovascular risk profile and ultimately an increased incidence of cardiovascular disease (CVD), risks that may similarly extend to women with milder gestational glucose intolerance as well. Thus, by providing a unique window to a woman's risk potential for future metabolic and vascular disease, glucose tolerance testing in pregnancy, as currently practiced, may offer an opportunity for the early identification of high-risk individuals prior to the onset of clinical disease. Ultimately, the insight so derived may inform strategies for postpartum surveillance, risk factor modification, and disease prevention that may eventually lead to a reduction in the burden of T2DM and CVD in women.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".