1119-P: Alleviating Carbohydrate Counting Burden in Type 1 Diabetes (T1D) with the Artificial Pancreas and Empagliflozin (EMPA)
Bibliographic record
Abstract
We aimed to assess whether adding Empa to the artificial pancreas can alleviate the burden of carbohydrate counting without degrading glucose control. We conducted a randomized, open-label, crossover, non-inferiority trial in 30 adults with T1D (age 40±15 years, A1c 7.6±0.7%). Each participant used the artificial pancreas on 5 non-consecutive days at home (9-14 hours/day) with: Empa 25mg and (i) carbohydrate counting, (ii) simple meal announcements (pressing a button, no carbohydrate counting), and (iii) no meal announcements (fully automated), and with: no Empa and (iv) carbohydrate counting (control arm) and (v) simple meal announcements. The fully automated artificial pancreas with Empa was inferior to the artificial pancreas with carbohydrate counting without Empa (mean glucose 10.0±1.6 vs. 8.5±1.5 mmol/L, p<0.001), but the artificial pancreas with simple meal announcement and Empa was non-inferior (8.5±1.4 mmol/L, non-inferiority p-value with a pre-specified margin of 0.75 mmol/L = 0.003). Time spent below 3.9 mmol/L was minimal in all interventions. No diabetic ketoacidosis was observed; mean ketones levels were 0.22±0.18 and 0.13±0.11 mmol/L, with and without Empa, respectively (p<0.001). We conclude that SGLT2 inhibition added to the artificial pancreas may alleviate the need for carbohydrate counting but does not allow a fully closed-loop system. Disclosure A. Haidar: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., Eli Lilly and Company. J. Yale: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk A/S, Sanofi. Board Member; Self; Diagnos. Research Support; Self; Bayer Inc., Novo Nordisk A/S, Sanofi. Speaker’s Bureau; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Medtronic, Novo Nordisk A/S, Sanofi. L. Lovblom: None. N. Cardinez: None. A. Orszag: None. C.M. Falappa: None. N. Gouchie-Provencher: None. A. El Fathi: None. D. Eldelekli: None. D. Scarr: None. B.A. Perkins: Consultant; Self; Abbott, Boehringer Ingelheim International GmbH, Insulet Corporation. Research Support; Self; Boehringer Ingelheim International GmbH, Novo Nordisk A/S. Other Relationship; Self; Medtronic. Funding Diabetes Canada
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".