Efficacy of an Overnight Predictive Low-Glucose Suspend System in Relation to Hypoglycemia Risk Factors in Youth and Adults With Type 1 Diabetes
Bibliographic record
Abstract
BACKGROUND: We developed a system to suspend insulin pump delivery overnight when the glucose trend predicts hypoglycemia. This predictive low-glucose suspend (PLGS) system substantially reduces nocturnal hypoglycemia without an increase in morning ketosis. Evaluation of hypoglycemia risk factors that could potentially influence the efficacy of the system remains critical for understanding possible problems with the system and identifying patients that may have the greatest benefit when using the system. METHODS: The at-home randomized trial consisted of 127 study participants with hemoglobin A1c (A1C) of ≤8.5% (mmol/mol) for patients aged 4-14 years and ≤8.0% for patient aged 15-45 years. Factors assessed included age, gender, A1C, diabetes duration, daily percentage basal insulin, total daily dose of insulin (units/kg-day), bedtime BG, bedtime snack, insulin on board, continuous glucose monitor (CGM) rate of change (ROC), day of the week, time system activated, daytime exercise intensity, and daytime CGM-measured hypoglycemia. RESULTS: The PLGS system was effective in preventing hypoglycemia for each factor subgroup. There was no evidence that the PLGS system was more or less effective in preventing hypoglycemia in any one subgroup compared with the other subgroups based on that factor. In addition, the effect of the system on overnight hyperglycemia did not differ in subgroups. CONCLUSIONS: The PLGS system tested in this study effectively reduced hypoglycemia without a meaningful increase in hyperglycemia across a variety of factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".