Test–Retest Reliability of a Continuous Glucose Monitoring System in Individuals with Type 2 Diabetes
Bibliographic record
Abstract
AIMS: This study determined the test-retest reliability of a continuous glucose monitoring system (CGMS) (iPro™2; Medtronic, Northridge, CA) under standardized conditions in individuals with type 2 diabetes (T2D). SUBJECTS AND METHODS: Fourteen individuals with T2D spent two nonconsecutive days in a calorimetry unit. On both days, meals, medication, and exercise were standardized. Glucose concentrations were measured continuously by CGMS, from which daily mean glucose concentration (GLU(mean)), time spent in hyperglycemia (t(>10.0 mmol/L)), and meal, exercise, and nocturnal mean glucose concentrations, as well as glycemic variability (SD(w), percentage coefficient of variation [%cv(w)], mean amplitude of glycemic excursions [MAGEc, MAGE(ave), and MAGE(abs.gos)], and continuous overlapping net glycemic action [CONGA(n)]) were estimated. Absolute and relative reliabilities were investigated using coefficient of variation (CV) and intraclass correlation, respectively. RESULTS: Relative reliability ranged from 0.77 to 0.95 (P<0.05) for GLU(mean) and meal, exercise, and nocturnal glycemia with CV ranging from 3.9% to 11.7%. Despite significant relative reliability (R=0.93; P<0.01), t(>10.0 mmol/L) showed larger CV (54.7%). Among the different glycemic variability measures, a significant between-day difference was observed in MAGEc, MAGE(ave), CONGA6, and CONGA12. The remaining measures (i.e., SD(w), %cv(w), MAGE(abs.gos), and CONGA1-4) indicated no between-day differences and significant relative reliability. CONCLUSIONS: In individuals with T2D, CGMS-estimated glycemic profiles were characterized by high relative and absolute reliability for both daily and shorter-term measurements as represented by GLUmean and meal, exercise, and nocturnal glycemia. Among the different methods to calculate glycemic variability, our results showed SD(w), %cv(w), MAGE(abs.gos), and CONGAn with n ≤ 4 were reliable measures. These results suggest the usefulness of CGMS in clinical trials utilizing repeated measured.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".