Treating to Target Glycaemia in Type 2 Diabetes Pregnancy
Bibliographic record
Abstract
There is an increasing awareness that in those who develop early-onset (18-39 years) adult type 2 diabetes, an increase in insulin resistance, deterioration in beta-cell, and clustering of cardiovascular risk factors are particularly pronounced. Pregnant women with type 2 diabetes have additional risk factors for serious adverse pregnancy outcomes as well as added barriers regarding healthcare access before, during, and after pregnancy. Compared to pregnant women with type 1 diabetes, those with type 2 diabetes are older, have higher body mass index (BMI), with more metabolic comorbidities and concomitant medications, are more likely to belong to minority ethnic groups, and live in the highest areas of socio-economic deprivation. Approximately, one in seven pregnant women with type 2 diabetes (median age 34 years) are taking ACE-inhibitors, statins (13%), and/or other potentially harmful diabetes therapies (7%). Fewer than one in four are taking a high dose of folic acid before pregnancy, which may suggest that planning for pregnancy is not a priority for women themselves, their healthcare professionals, or the healthcare system. Knowledge of the epidemiology, pathophysiology, and unique management considerations of early-onset type 2 diabetes is essential to providing evidence-based care to pregnant women with type 2 diabetes. This narrative review will discuss contemporary data regarding type 2 diabetes pregnancy outcomes and the increasing recognition that different types of diabetes may require different treatment strategies before, during, and after pregnancy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".