Timing of <scp>CGM</scp> initiation in pediatric diabetes: The <scp>CGM TIME</scp> Trial
Bibliographic record
Abstract
OBJECTIVE: To determine whether timing of CGM initiation offering low glucose suspend (LGS) affects CGM adherence in children and youth starting insulin pump therapy. METHODS: A 5-site RCT of pump-naïve subjects (aged 5-18 years) with type 1 diabetes (T1D) for at least 1 year compared simultaneous pump and CGM initiation offering LGS vs standard pump therapy with CGM initiation delayed for 6 months. Primary outcome was CGM adherence (hours per 28 days) (MiniMed™ Paradigm™ Veo™ system; CareLink Pro™ software) over 6 months after CGM initiation. Secondary outcome HbA1c was measured centrally. Linear mixed-models and ordinary least squares models were fitted to estimate effect of intervention, and covariates baseline age, T1D duration, HbA1c, gender, ethnicity, hypoglycemia history, clinical site, and association between CGM adherence and HbA1c. RESULTS: The trial randomized 144/152 (95%) eligible subjects. Baseline mean age was 11.5 ± 3.3(SD) years, T1D duration 3.4 ± 3.1 years, and HbA1c 7.9 ± 0.9%. Six months after CGM initiation, adjusted mean difference in CGM adherence was 62.4 hours per 28 days greater in the Simultaneous Group compared to Delayed Group (P = .007). There was no difference in mean HbA1c at 6 months. However, for each 100 hours of CGM use per 28-day period, HbA1c was 0.39% (95% CI 0.10%-0.69%) lower. Higher CGM adherence was associated with reduced time with glucose >10 mmol/L (P < .001). CONCLUSION: CGM adherence was higher after 6 months when initiated at same time as pump therapy compared to starting CGM 6 months after pump therapy. Greater CGM adherence was associated with improved HbA1c.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".