Intensive Glycemic Treatment During Type 1 Diabetes Pregnancy: A Story of (Mostly) Sweet Success!
Bibliographic record
Abstract
Studies from Scotland and Canada confirm large increases in the incidence of pregnancies complicated by pregestational type 1 diabetes (T1D). With this increased antenatal workload comes more specialization and staff expertise, which may be important as diabetes technology use increases. While euglycemia remains elusive and obstetrical intervention (earlier delivery, increased operative deliveries) is increasing, there have been some notable successes in the past 5-10 years. These include a decline in the rates of congenital anomaly (Canada) and stillbirths (U.K.) and substantial reductions in both maternal hypoglycemia (both moderate and severe) across many countries. However, pregnant women with T1D still spend ∼30-45% of the time (8-11 h/day) hyperglycemic during the second and third trimesters. The duration of maternal hyperglycemia appears unchanged in routine clinical care over the past decade. This ongoing fetal exposure to maternal hyperglycemia likely explains the persistent rates of large for gestational age (LGA), neonatal hypoglycemia, and neonatal intensive care unit (NICU) admissions in T1D offspring. The Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT) found that pregnant women using real-time continuous glucose monitoring (CGM) spent 5% less time (1.2 h/day) hyperglycemic during the third trimester, with clinically relevant reductions in LGA, neonatal hypoglycemia, and NICU admissions. This article will review the progress in our understanding of the intensive glycemic treatment of T1D pregnancy, focusing in particular on the recent technological advances in CGM and automated insulin delivery. It suggests that even with advanced diabetes technology, optimal maternal dietary intake is needed to minimize the neonatal complications attributed to postprandial hyperglycemia.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".