Effects of Sensor-Augmented Pump Therapy on Glycemic Variability in Well-Controlled Type 1 Diabetes in the STAR 3 Study
Bibliographic record
Abstract
BACKGROUND: Compared with multiple daily injections (MDI), sensor-augmented pump (SAP) insulin therapy may reduce glycemic variability and oxidative stress in type 1 diabetes in a glycosylated hemoglobin (A1C)-independent manner. SUBJECTS AND METHODS: The STAR 3 study compared SAP with MDI therapy for 1 year. Week-long continuous glucose monitoring studies were conducted at baseline and 1 year for assessment of glycemic variability in both groups. Soluble CD40 ligand (CD40L), a biomarker of inflammation and thrombocyte function, was measured at baseline and 1 year. Subjects were classified according to treatment group and 1-year A1C levels (<6.5%, 6.5-6.9%, 7-7.9%, ≥8%). Glycemic parameters were compared between SAP and MDI subjects in each A1C cohort. RESULTS: At 1 year, sensor glucose values at A1C levels ≥6.5% were similar in the SAP and MDI groups. However, sensor glucose SD and coefficient of variation (CV) values were lower at A1C levels <8% among SAP than among MDI subjects; the overall between-group difference was significant for both SD (P<0.01) and CV (P=0.01). The overall mean amplitude of glycemic excursion was similar in MDI and SAP groups (P=0.23). CD40L levels fell over the course of the study in both groups, but the between-group difference was not significant (P=0.18). CD40L concentrations were unrelated to A1C, change in A1C from baseline, or glycemic variability. CONCLUSIONS: At comparable A1C levels of <8%, SAP reduced glycemic variability as measured by SD and CV compared with MDI. SAP may provide beneficial reductions in the number and severity of glycemic excursions.
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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 | 0.002 |
| 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 | 0.001 | 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".