Supported Open-Source Automated Insulin Delivery for Management of Type 1 Diabetes in Pregnancy
Bibliographic record
Abstract
BACKGROUND: The tight glycemia required to optimize type 1 diabetes (T1D) pregnancy outcomes is difficult to achieve with standard insulin therapies. Automated insulin delivery (AID) offers an avenue to improve glycemia, but most available systems are not configurable to tight pregnancy glucose targets. Open-source AID may meet the needs of some pregnant women with T1D, but available data on its efficacy and safety in pregnancy are limited. METHODS: This single-center retrospective study describes the glycemic and obstetric outcomes of pregnancies in which supported open-source AID (SOSAID) was used. Included patients had a pregnancy managed on SOSAID at BCDiabetes between January 2023 and October 2024 and consented for inclusion of their clinical data. Charts were reviewed to obtain comprehensive glycemic data, obstetric outcomes, and adverse events. RESULTS: ) was 68% in trimester 2 and 70% in trimester 3. Seven patients commenced SOSAID during pregnancy, with their median 14-day TIR rising from 52% pre-SOSAID to 71% immediately after commencing SOSAID. There were no perinatal deaths or congenital anomalies. Pre-term delivery occurred in 1/10 and hypertensive disorders of pregnancy occurred in 2/10 women. Birthweight above 4 kg was present in 3/10, and neonatal hypoglycemia occurred in 4/10. CONCLUSIONS: SOSAID systems represent a promising tool for managing T1D in pregnancy and were successful in reaching target pregnancy glycemia in this single-center cohort.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".