Group Medical Visits (GMVs) in primary care: an RCT of group-based versus individual appointments to reduce HbA1c in older people
Bibliographic record
Abstract
INTRODUCTION: Type 2 diabetes mellitus (T2DM) affects more than 1.1 million Canadians aged ≥65 years. Group Medical Visits are an emerging health service delivery method. Recent systematic reviews show that they can significantly reduce glycated haemoglobin (HbA1c) levels, but Group Visits have not been evaluated within primary care. We intend to determine the clinical effectiveness, quality of life and economic implications of Group Medical Visits within a primary care setting for older people with T2DM. METHODS AND ANALYSIS: A 2-year proof-of-concept, single-blinded (measurement team) randomised control trial to test the efficacy of Group Medical Visits in an urban Canadian primary care setting. Participants ≥65 years old with T2DM (N=128) will be equally randomised to either eight groups of eight patients each (Group Medical Visits; Intervention) or to Individual visits (Standard Care; Controls). Those administering cointerventions are not blinded to group assignment. Our sample size is based on estimates of variance (±1.4% for HbA1c) and effect size (0.9/1.4=0.6) from the literature and from our own preliminary data. Forty participants per group will provide a β likelihood of 0.80, assuming an α of 0.05. A conservative estimation of an effect size of 0.7/1.4 changes the N in the power calculation to 59 per group. Hence, we aim to enrol 64 participants in each study arm. We will use intention-to-treat analysis and compare mean HbA1c (% glycosylated HbA1c) (primary outcome) of Intervention/Control participants at 12 months, 24 months and 1 year postintervention on selected clinical, patient-rated and economic measures. TRIAL REGISTRATION NUMBER: NCT02002143.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".