A Simplified Semiquantitative Meal Bolus Strategy Combined with Single- and Dual-Hormone Closed-Loop Delivery in Patients with Type 1 Diabetes: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Single- and dual-hormone closed-loop systems can improve glycemic control and have the potential to reduce carbohydrate-counting burden for patients with type 1 diabetes; however, simplification of meal insulin calculation should not compromise glycemic control. METHODS: We compared in a randomized outpatient pilot trial: (1) a single-hormone closed-loop system accompanied with carbohydrate-content matched boluses versus accompanied with a simplified meal bolus strategy, and (2) a dual-hormone closed-loop system accompanied with carbohydrate-content matched boluses versus accompanied with a simplified meal bolus strategy. Carbohydrate-matched boluses were based on the participant's carbohydrate meal content estimation whereas the simplified strategy involved the selection, by participants, of a semi-quantitative meal carbohydrate-content size: snack, regular, large, or very large meal. Each participant also underwent sensor-augmented pump therapy. Basal insulin delivery was more aggressive with the simplified bolus. The primary outcome was mean sensor glucose level over a 15-h daytime period. RESULTS: Twelve participants were recruited (48.2 ± 16.0 years old; HbA1c 7.4% ± 0.9%) to compare the two bolus strategies during single- and dual-hormone closed-loop delivery. A similar mean sensor glucose level (15 h) was achieved with the carbohydrate-matched boluses and simplified strategy using single-hormone (median [interquartile]: 7.6 [7.2-8.1] vs. 8.0 [7.0-8.6] mmol/L; P = 0.90) and dual-hormone closed-loop systems (7.6 [6.7-9.1] vs. 7.0 [6.4-8.2] mmol/L; P = 0.08). Exploratory analyses showed that, as compared with sensor-augmented pump therapy, there was an increased time spent in hypoglycemia with the simplified strategy but not with the carbohydrate-matched boluses. CONCLUSIONS: Though the algorithm employed in this pilot study may lead to an increased risk for hypoglycemia, this strategy has the potential to reduce the carbohydrate-counting burden in patients with type 1 diabetes while generally maintaining adequate glucose control. Longer outpatient studies with an improved algorithm are needed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".