Real‐world use of Control‐<scp>IQ</scp>™ technology automated insulin delivery in pregnancy: A case series with qualitative interviews
Bibliographic record
Abstract
BACKGROUND: Most commercially available automated insulin delivery (AID) systems are not approved for pregnancy use. Information regarding use of the Tandem t:slim X2 insulin pump with Control-IQ™ technology in pregnancy is lacking. AIMS: This case series aimed to explore glycaemic and qualitative experiences of four early adopters of Control-IQ technology in pregnancy. METHODS: Participants used Control-IQ technology in pregnancy and postpartum and consented to analysis of glycaemic data and semi-structured interviews. RESULTS: Case 1 began Control-IQ technology at 10 weeks gestation. Her pregnancy glucose time-in-range (3.5-7.8 mmol/L [63-140 mg/dL]) increased from 58.7% to 73.3% by third trimester. Cases 2-4 began using Control-IQ technology 0-2 months preconception. Pregnancy time-in-range glucose increased from 73.4% to 78.7%, 78% to 83.6%, and 46.5% to 71.9% between first and third trimesters, respectively. A mid-pregnancy decline in time-in-range glucose was observed in two of the four participants related to suboptimal pump setting adjustments and delays in sensor and infusion set replacement. No diabetic ketoacidosis or severe hypoglycaemia occurred. All participants reported reduced diabetes management burden and improved sleep with Control-IQ technology use. CONCLUSIONS: Early adopters of Control-IQ technology safely used this system off-label in pregnancy and reported reduced diabetes management burden and improved sleep. The largest glycaemic improvements were observed among those with the lowest pregnancy time-in-range glucose at the beginning of pregnancy. Participants with low pregnancy glucose time-in-range increased their time-in-range with Control-IQ technology use and participants with high pregnancy glucose time-in-range maintained and increased their time-in-range with less diabetes management burden.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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".