Epidemiology and Therapeutic Strategies for Women With Preexisting Diabetes in Pregnancy: How Far Have We Come? The 2021 Norbert Freinkel Award Lecture
Bibliographic record
Abstract
The field of diabetes in pregnancy has witnessed tremendous changes over the past 30 years, with an explosive growth in case numbers along with new and exciting opportunities to affect outcomes. Type 1 diabetes in pregnancy has increased by 40%, but type 2 diabetes in pregnancy, rarely seen 30 years ago, has more than doubled and, in some cases, tripled in prevalence. Compared with women with type 2 diabetes, women with type 1 diabetes have higher HbA1c, more large-for-gestational-age infants, and more preterm births. Women with type 2 diabetes have more chronic hypertension, more socioeconomic deprivation, and higher rates of perinatal mortality. Large randomized trials in women with diabetes in pregnancy have helped us understand the effectiveness of new technologies (i.e., continuous glucose monitoring) in women with type 1 diabetes, and the addition of metformin to insulin in women with type 2 diabetes, in improving pregnancy outcomes. Future endeavors, including artificial pancreas systems in women with type 1 diabetes and the use of continuous glucose monitoring, a better understanding of nutrition during pregnancy, and approaches to improve preconception and pregnancy self-care in women with type 2 diabetes, may lead to further improved outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".