Telecare for Patients With Type 1 Diabetes and Inadequate Glycemic Control
Bibliographic record
Abstract
OBJECTIVE: To determine the efficacy of telecare (modem transmission of glucometer data and clinician feedback) to support intensive insulin therapy in patients with type 1 diabetes and inadequate glycemic control. RESEARCH DESIGN AND METHODS: Thirty-one patients with type 1 diabetes on intensive insulin therapy and with HbA1c >7.8% were randomized to telecare (glucometer transmission with feedback) or control (glucometer transmission without feedback) for 6 months. The primary end point was 6-month HbA1c. To place our findings in context, we pooled HbA1c change from baseline reported in randomized trials of telecare identified in a systematic review of the literature. RESULTS: Compared with the control group, telecare patients had a significantly lower 6-month HbA1c (8.2 vs. 7.8%, P = 0.03, after accounting for HbA1c at baseline) and a nonsignificant fourfold greater chance of achieving 6-month HbA1c < or =7% (29 vs. 7%; risk difference 21.9%, 95% CI -4.7 to 50.5). Nurses spent 50 more min/patient giving feedback on the phone with telecare patients than with control patients. Meta-analysis of seven randomized trials of adult patients with type 1 diabetes found a 0.4% difference (95% CI 0-0.8) in HbA1c mean change from baseline between the telecare and control groups. CONCLUSIONS: Telecare is associated with small effects on glycemic control in patients with type 1 diabetes on intensive insulin therapy but with inadequate glycemic control.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".