A Pilot Outpatient Assessment of a Fully Closed-Loop Insulin and Pramlintide System
Bibliographic record
Abstract
BACKGROUND: Type 1 diabetes is treated with exogenous insulin using multiple daily injections or insulin pumps. However, both strategies require carbohydrate counting for prandial insulin dosing, which is both burdensome and error prone. METHODS: We conducted a pilot, randomized, controlled study to eliminate carbohydrate counting in adults (n = 12, 7 females, age 39.5 [15.1], HbA1c 7.4% [0.6]) using an automated insulin and pramlintide fully closed-loop system. The interventions included five arms during which participants underwent 14 hours of outpatient, free-living, supervised experiments of (1) faster aspart with carbohydrate counting (control), faster aspart and pramlintide without carbohydrate counting at (2) 8 µg/U and (3) 10 µg/U ratios, and aspart and pramlintide without carbohydrate counting at (4) 8 µg/U and (5) 10 µg/U ratios. RESULTS: The median time in target range (3.9-10.0 mmol/L) with the control arm was 78.6 [65.3-92.9], compared with 76.2 [64.6-86.9] and 78.8 [68.8-86.0] with the fully closed-loop faster aspart and pramlintide systems at 8 and 10 µg/U ratios, respectively, and compared with 65.9 [59.9-83.6] and 77.4 [72.1-82.7] with the fully closed-loop aspart and pramlintide systems at 8 and 10 µg/U ratios, respectively. Times spent below 3.9 and 3.0 mmol/L were numerically higher with the fully closed-loop aspart and pramlintide systems than the control arm. None of the differences were statistically significant. CONCLUSIONS: This study suggests that automated insulin and pramlintide systems have the potential to alleviate carbohydrate counting without degrading time in range. A longer and larger study is underway.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".