Predictive Hyperglycemia and Hypoglycemia Minimization: In-Home Evaluation of Safety, Feasibility, and Efficacy in Overnight Glucose Control in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE The objective of this study was to determine the safety, feasibility, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system compared with predictive low-glucose insulin suspension (PLGS) alone in overnight glucose control. RESEARCH DESIGN AND METHODS A 42-night trial was conducted in 30 individuals with type 1 diabetes in the age range 15–45 years. Participants were randomly assigned each night to either PHHM or PLGS and were blinded to the assignment. The system suspended the insulin pump on both the PHHM and PLGS nights for predicted hypoglycemia but delivered correction boluses for predicted hyperglycemia on PHHM nights only. The primary outcome was the percentage of time spent in a sensor glucose range of 70–180 mg/dL during the overnight period. RESULTS The addition of automated insulin delivery with PHHM increased the time spent in the target range (70–180 mg/dL) from 71 ± 10% during PLGS nights to 78 ± 10% during PHHM nights (P < 0.001). The average morning blood glucose concentration improved from 163 ± 23 mg/dL after PLGS nights to 142 ± 18 mg/dL after PHHM nights (P < 0.001). Various sensor-measured hypoglycemic outcomes were similar on PLGS and PHHM nights. All participants completed 42 nights with no episodes of severe hypoglycemia, diabetic ketoacidosis, or other study- or device-related adverse events. CONCLUSIONS The addition of a predictive hyperglycemia minimization component to our existing PLGS system was shown to be safe, feasible, and effective in overnight glucose 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".