Gestational prediabetes: a new term for early prevention?
Bibliographic record
Abstract
Women with gestational diabetes mellitus (GDM) have higher rates of foetal macrosomia, shoulder dystocia and pregnancy-induced hypertension, and are at higher risk of developing type 2 diabetes. Herein, we introduce a new conceptual term, "gestational prediabetes", which requires the absence of diabetes before pregnancy, and the presence of blood glucose levels (or a related marker) in early pregnancy that are higher than normal, but not yet high enough to meet the diagnostic criteria for GDM. Identifying women with gestational prediabetes might be done in early pregnancy (e.g., 12 weeks' gestation) using conventional glycaemic testing, assessment of visceral abdominal adiposity or hepatic fat by ultrasonography, or measuring serum sex hormone-binding globulin or adiponectin. However, none of these approaches has been systematically compared to conventional predictors, such as maternal body mass index or waist circumference. Any early-pregnancy predictor of gestational prediabetes risk needs to have low cost, ease of administration, and a short turnaround time. The theoretical advantage of identifying women with gestational prediabetes would be to "prevent" the onset of GDM (and its inherent risks to the pregnancy) in a timelier manner. One sensible starting point would be an intervention to prevent early excessive weight gain in pregnancy, which is currently being evaluated by two randomized clinical trials. In addition, early intervention could offset the need for resource-intense GDM management or insulin therapy.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".