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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".