758-P: A Randomized Crossover Trial to Compare Automated Insulin Delivery with Carbohydrate Counting or Simplified Qualitative Meal-Size Estimation in Type 1 Diabetes
Bibliographic record
Abstract
We did a randomized crossover non-inferiority trial to compare 3 weeks of automated insulin delivery with (i) carbohydrate counting and (ii) qualitative meal-size estimation (low, medium, high, or very high carbohydrate (CHO)) in 30 adults with T1D (20/30 females, age 44±17 yrs, A1c 7.4±0.7%) . Low, medium, high, and very high CHO meals were defined as <30 g CHO, 30-60 g CHO, 60-90 g CHO, and >90 g CHO, respectively, and their prandial insulin boluses were calculated as the individualized insulin-to-CHO ratios x 15, 35, 65, and 95, respectively. Closed-loop algorithms were otherwise identical in the two arms. The time in range 3.9-10.0 mmol/L (primary outcome) was 74% (SD 10%) with carbohydrate counting and 71% (11%) with qualitative meal-size estimation; difference -3.6% (95% CI, -0.6% to -6.5%) which crossed the pre-specified non-inferiority margin of 4%. Times <3.9 mmol/L and <3.0 mmol/L were low in both two arms. Automated basal insulin delivery was higher in the qualitative meal-size estimation arm (34.6 vs. 32.6 u/day, p=0.003) . We conclude that non-inferiority of the qualitative meal-size estimation was not confirmed, though this method achieved a high time in range and low time in hypoglycemia. The qualitative meal-size estimation method may benefit from larger prandial boluses and more responsive post-meal automatic basal delivery. Disclosure A.Haidar: Consultant; Eli Lilly and Company, Research Support; ADOCIA, Dexcom, Inc., Eli Lilly and Company, Tandem Diabetes Care, Inc. L.Legault: Advisory Panel; Abbott Diabetes, Insulet Corporation, Novo Nordisk A/S, Other Relationship; Eli Lilly and Company, Research Support; AstraZeneca, Merck & Co., Inc. M.Raffray: None. N.Gouchie-provencher: None. A.Jafar: None. M.Devaux: None. M.Ghanbari: None. R.Rabasa-lhoret: Consultant; HLS Therapeutics Inc., Pfizer Inc., Other Relationship; Abbott Diabetes, AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Novo Nordisk Canada Inc., Sanofi, Vertex Pharmaceuticals Incorporated, Research Support; Canadian Institutes of Health Research, Cystic Fibrosis Canada, Diabetes Canada, Fondation Francophone pour la Recherche en Diabète (FFRD) , JDRF, National Institutes of Health, Société Francophone du Diabète (SFD) , Speaker's Bureau; Canadian Medical & Surgical Knowledge Translation Research Group (CMS) , CPD Network, Tandem Diabetes Care, Inc. Funding National Institutes of Health (1DP3DK106930-01)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".