Use of Virtual Care for Glycemic Management in People With Types 1 and 2 Diabetes and Diabetes in Pregnancy: A Rapid Review
Bibliographic record
Abstract
Our objective in this study was to answer the main research question: In patients with diabetes, does virtual care vs face-to-face care provide different clinical, patient and practitioner experience or quality outcomes? Articles (2012 to 2020) describing interventions using virtual care with the capability for 2-way, individualized interactions compared with usual care were included. Studies involving any patients with diabetes and outcomes of glycated hemoglobin (A1C), quality of care and/or patient or health-care practitioner experience were included. Systematic reviews, randomized controlled studies, quasi-experimental trials, implementation trials, observational studies and qualitative analyses were reviewed. MEDLINE and McMaster Health Evidence databases searched in June 2020 identified 59 articles. Virtual care, in particular telemonitoring, combined with a means of 2-way communications provided improvement in A1C similar or superior to usual care, with the strongest evidence for type 2 diabetes. Virtual care was generally acceptable to patients, who expressed satisfaction with their care. Health-care providers recognized benefits but raised issues of technical support, workflow and compensation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".