Closed-Loop Insulin Delivery in Type 1 Diabetes in Pregnancy
Bibliographic record
Abstract
Importance: Hyperglycemia-related pregnancy complications occur in 50% of pregnant women with type 1 diabetes. Closed-loop insulin systems improve glycemia outside of pregnancy but have had limited testing in pregnancy. Objective: To assess the efficacy of a closed-loop system in pregnancy. Design, Setting, and Participants: Open-label trial enrolling pregnant women with type 1 diabetes at 14 clinical centers in Canada and Australia before 14 weeks' gestation with follow-up until 6 weeks postpartum. Enrollment occurred between June 2021 and July 2024 and follow-up was completed in March 2025. Interventions: Participants were randomized 1:1 to closed-loop therapy (n = 46) or standard care (insulin pump or multiple daily insulin injections) (n = 45) with continuous glucose monitoring. Main Outcomes and Measures: The primary outcome was the percentage of time spent in the pregnancy-specific glucose range (63-140 mg/dL), measured by continuous glucose monitoring from 16 to 34 weeks' gestation. Results: Among 94 enrolled participants, 3 experienced pregnancy loss prior to randomization, 91 were randomized (mean age, 31.7 [SD, 5.2] years; early pregnancy hemoglobin A1c, 7.4% [SD, 1.0%]), and 88 were included in the primary analysis. The mean percentage of time spent in the pregnancy-specific glucose range from 16 to 34 weeks' gestation was 65.4% in the closed-loop group and 50.3% in the standard care group (mean adjusted difference, 12.5 [95% CI, 9.5-15.6] percentage points; P < .001). There was 1 episode of severe hypoglycemia in the closed-loop group, and there were 2 episodes of diabetic ketoacidosis in the closed-loop group and 1 in the standard care group. Conclusion and Relevance: Pregnant women with type 1 diabetes using a closed-loop system spent significantly more time in the pregnancy-specific glucose range than those receiving standard care. These findings support the use of this closed-loop system in pregnant women with type 1 diabetes. Trial Registration: ClinicalTrials.gov Identifier: NCT04902378.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".