Effects of Continuous Glucose Monitoring Versus Blood Glucose Monitoring During a Carbohydrate-Restricted Nutrition Intervention in People With Type 2 Diabetes: 6-Month Follow-up Outcomes From a Randomized Clinical Trial
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Bibliographic record
Abstract
OBJECTIVES: Low and very-low carbohydrate eating patterns can improve glycemia in people with type 2 diabetes (T2D). Continuous glucose monitoring (CGM) may also help improve glycemic outcomes, like time in range (TIR). This research evaluated differences in diabetes-related outcomes when people with T2D used CGM or blood glucose monitoring (BGM) to support dietary choices and medication management for 6 months during a virtual, medically supervised ketogenic diet program (MSKDP). Three-month primary outcomes are published, and here we report 6-month follow-up outcomes. METHODS: The IGNITE study (Impact of Glucose moNitoring and nutrItion on Time in rangE) randomized participants to use CGM (N = 81) or BGM (N = 82) to support care during 6 months in a MSKDP. Glycemia, diabetes medications, dietary intake, ketones, and weight were assessed at baseline (Base) and month 6 (M6); differences between and within arms were evaluated. RESULTS: Adults (N = 163) with mean (SD) T2D duration of 9.7 (7.7) years and HbA1c of 8.1% (1.2%) participated. From Base to M6, TIR improved from 61% to 87% for CGM and from 63% to 88% for BGM (P < .001), with no difference in changes between arms (P = .99). HbA1c decreased at least 1.3% from Base to M6 in both arms (P < .001). Diabetes medications were deintensified in both arms based on medication effect scores (P < .01). Energy and carbohydrate intake decreased (P < .001) and participants in both arms had clinically meaningful weight loss (P < .001). CONCLUSIONS: The CGM and BGM arms achieved similar and significant improvements in glycemia and other diabetes-related outcomes after 6 months in this MSKDP.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it