Intensive remote monitoring versus conventional care in type 1 diabetes: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: While frequent contact with diabetes care providers may improve glycemic control among patients with type 1 diabetes (T1D), in-person visits are labor-intensive and costly. This study was conducted to assess the impact of an intensive remote therapy (IRT) intervention for pediatric patients with T1D. METHODS: Pediatric patients with T1D were randomized to IRT or conventional care (CC) for 6 months. Both cohorts continued routine quarterly clinic visits and uploaded device data; for the IRT cohort, data were reviewed and patients were contacted if regimen adjustments were indicated. Glycated hemoglobin (HbA1c) change from baseline was assessed at 6 and 9 months. Diabetes-related quality of life (QoL), healthcare services utilization, and hypoglycemic events were also tracked. RESULTS: Among 117 enrollees (60 IRT, 57 CC), mean (SD) 6-month %HbA1c change for IRT vs CC was -0.34 (0.85) (-3.7 mmol/mol) vs -0.05 (0.74) (-0.5 mmol/mol) overall (P = .071); -0.15 (0.67) (1.6 mmol/mol) vs -0.02 (0.66) (0.2 mmol/mol) for ages 8 to 12 (P = .541); and -0.50 (0.95) (-5.5 mmol/mol) vs -0.06 (0.80) (-0.7 mmol/mol) for ages 13 to 17 (P = .056). Diabetes-related QoL increased by 6.5 and 1.3 points for IRT and CC, respectively (P = .062). Three months after intervention cessation, %HbA1c changed minimally among treated children aged 8 to 12 but increased by 0.22 (0.89) (2.4 mmol/mol) among those aged 13 to 17. CONCLUSIONS: IRT substantially affected diabetes metrics and improved QoL among pediatric patients with T1D. Adolescents experienced a stronger treatment effect, but had difficulty in sustaining improved control after intervention cessation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".