The effectiveness of continuous glucose monitoring with remote telemonitoring-enabled virtual educator visits in adults with non-insulin dependent type 2 diabetes: A randomized trial
Bibliographic record
Abstract
AIMS: Estimate the effectiveness of continuous glucose monitoring (CGM) with remote telemonitoring-enabled virtual diabetes educator visits for improving glycemic management in adults with type 2 diabetes, not on insulin. METHODS: Participants with type 2 diabetes, not on insulin, and HbA1c > 7.0 % were enrolled in an open-label randomized trial of 6 weeks of CGM with telemonitoring versus enhanced usual care. Both groups received educator visits. HbA1c was assessed at 12 weeks. RESULTS: Of 105 participants (mean age 57.3 years, 49.5 % females, mean baseline HbA1c 8.0 %), 86 remained at follow-up. Change in HbA1c was -0.69 % (CGM) versus -0.33 % (enhanced usual care). Adjusting for baseline HbA1c, CGM was superior (0.65 % greater HbA1c reduction [95 % CI 0.17-1.12 %], p = 0.008). CGM participants were 92 % (RR = 1.92, 1.19-3.06, p = 0.007) more likely to have an HbA1c reduction ≥ 0.5 %, lost more weight (difference in weight reduction 2.17 kg, 0.22-4.11, p = 0.029) and were more satisfied with their treatment. No treatment-related adverse events were observed. CONCLUSIONS: CGM with virtual diabetes educator visits is effective, safe, and acceptable in adults with type 2 diabetes not on insulin and should be considered as an alternative to drug therapy for improving blood glucose.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".