Additive Benefits of Control-IQ+ AID to GLP-1 Receptor Agonist Use in Adults With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of automated insulin delivery (AID) on glycemic and insulin outcomes in adults with insulin-treated type 2 diabetes using a glucagon-like peptide-1 receptor agonist (GLP-1 RA). RESEARCH DESIGN AND METHODS: In a randomized trial comparing Control-IQ+ AID versus continuation of prestudy insulin delivery method plus continuous glucose monitoring (CGM group), 143 (45%) of the 319 participants were using a GLP-1 RA at baseline, which was continued during the trial. RESULTS: Among GLP-1 RA users, mean HbA1c decreased by 0.8% from a baseline of 8.0 ± 1.2% with AID, which represented a mean improvement of -0.5% (95% CI -0.8 to -0.3, P < 0.001) compared with the CGM group. Time-in-range 70-180 mg/dL and other CGM metrics reflective of hyperglycemia also showed comparable statistically significant improvements using AID when added to GLP-1 RA use. For GLP-1 RA users, there was no significant difference in weight after 13 weeks with AID compared with the CGM group (0.9 kg, 95% CI -0.2 to 2.1, P = 0.10), whereas, in GLP-1 RA nonusers, there was a mean weight gain of 1.9 kg with AID compared with CGM (95% CI 0.5 to 3.2, P = 0.007). CONCLUSIONS: The benefits of AID appear to be substantial for a broad spectrum of insulin-treated patients with type 2 diabetes, including those already receiving contemporary and guideline-directed therapy, such as a GLP-1 RA medication. These additive benefits of AID in GLP-1 RA users included significant reductions in HbA1c levels with simultaneous reduction in insulin use, along with no statistical increase in weight despite very significant improvements in glycemic control.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".