Comparison of Two Continuous Glucose Monitoring Systems, Dexcom G4 Platinum and Medtronic Paradigm Veo Enlite System, at Rest and During Exercise
Bibliographic record
Abstract
BACKGROUND: Despite technological advances, the accuracy of continuous glucose monitoring (CGM) systems may not always be satisfactory with rapidly changing glucose levels, as is notable during exercise. We compare the performance of two current and widely used CGM systems, Dexcom G4 Platinum (Dexcom) and Medtronic Paradigm Veo Enlite system (Enlite), during both rest and exercise in adults with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Paired sensor and plasma glucose (PG) values (total of 431 data pairs for Dexcom and 425 for Enlite) were collected from 17 adults (37.3 ± 13.6 years) with T1D. To evaluate and compare the accuracy of sensor readings, criteria involving sensor bias (sensor minus PG levels), absolute relative difference (ARD), and percentage of readings meeting International Organization for Standardization (ISO) criteria were considered. RESULTS: Both Dexcom and Enlite performed equally well during the rest period, with respective mean/median biases of -0.12/-0.02 mmol/L versus -0.18/-0.40 (P = 0.78, P = 0.66) mmol/L and ARDs of 13.77/13.34% versus 12.38/11.95% (P = 0.53, P = 0.70). During exercise, sensor bias means/medians were -0.40/-0.21 mmol versus -0.26/-0.24 mmol/L (P = 0.67, P = 0.62) and ARDs were 22.53/15.13% versus 20.44/14.11% (P = 0.58, P = 0.68) for Dexcom and Enlite, respectively. Both sensors demonstrated significantly lower performance during exercise; median ARD comparison at rest versus exercise for both Dexcom and Enlite showed a P = 0.02. More data pairs met the ISO criteria for Dexcom and Enlite at rest, 73.6% and 76.9% compared with exercise 48.2% and 53.9%. CONCLUSION: Dexcom and Enlite demonstrated comparable overall performances during rest and physical activity. However, a lower accuracy was observed during exercise for both sensors, necessitating a fine-tuning of their performance with physical activity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".