Effectiveness of group medical visits for improving diabetes care: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Group medical visits, whereby health care professionals meet with groups of patients who have the same disease, have been introduced in primary care as a way to meet the increasing demand for health care delivery to patients with chronic diseases. We performed a systematic review and meta-analysis of the evidence on the effectiveness of such visits for patients with diabetes. METHODS: We conducted a systematic review of all relevant studies published from 1947 to February 2012 identified in a search of electronic databases and grey literature. We included randomized controlled trials (RCTs) and observational studies published in English that included patients aged 16-80 years with type 1 or 2 diabetes and that had group medical visits as the intervention. These studies were assessed for methodologic quality. We included data only from the RCTs in the meta-analysis. RESULTS: Of the 94 studies identified, we selected 26 that met our inclusion criteria, 13 of which were RCTs. Group medical visits had a positive effect on clinical and patient-reported outcomes, with significant reductions in glycated hemoglobin (HbA1c reduction -0.46%, 95% confidence interval -0.80% to -0.31%). We were unable to assess the effect of group medical visits on processes of care because of an insufficient number of RCTs that reported on this outcome. INTERPRETATION: Group medical visits for patients with diabetes were found to be effective in terms of reducing HbA1c. The results of our meta-analysis suggest that wider implementation of group medical visits for patients with diabetes will have a positive effect on patient outcomes.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".