Adaptability of Closed Loop During Labor, Delivery, and Postpartum: A Secondary Analysis of Data from Two Randomized Crossover Trials in Type 1 Diabetes Pregnancy
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Bibliographic record
Abstract
Tight glucose control during labor and delivery is recommended for pregnant women with type 1 diabetes. This can be challenging to achieve using the current treatment modalities. The automated nature of closed loop and its ability to adapt to real-time glucose levels make it well suited for use during labor, delivery, and the immediate postpartum period. We report observational data of participants from two randomized crossover trials who chose to continue using closed loop during labor, delivery, and postpartum. Labor was defined as the 24 h before delivery and postpartum as the 48 h after delivery. The glucose target range during pregnancy was 3.5-7.8 mmol/L (63-140 mg/dL) and 3.9-10 mmol/L (70-180 mg/dL) after delivery. Twenty-seven (84.4%) of the potential 32 trial participants used closed loop through labor, delivery, and postpartum. Use of closed loop was associated with 82.0% (interquartile range [IQR] 49.3, 93.0) time-in-target range during labor and delivery and a mean glucose of 6.9 ± 1.4 mmol/L (124 ± 25 mg/dL). Closed loop performed well throughout vaginal, elective, and emergency cesarean section deliveries. Postpartum, women spent 83.3% (IQR 75.2, 94.6) time-in-target range (3.9-10.0 mmol/L [70-180 mg/dL]), with a mean glucose of 7.2 ± 1.4 mmol/L (130 ± 25 mg/dL). There was no difference in maternal glucose concentration between mothers of infants with and without neonatal hypoglycemia (6.9 ± 1.6 mmol/L and 6.8 ± 1.1 mmol/L [124 ± 29 mg/dL and 122 ± 20 mg/dL] respectively; P = 0.84). Automated closed-loop insulin delivery is feasible during hospital admissions for labor, delivery, and postpartum. Larger scale studies are needed to evaluate its efficacy compared with current clinical approaches as well as understand how women and healthcare providers will adopt this technology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it