The Impact of Web-Based Continuing Medical Education Using Patient Simulation on Real-World Treatment Selection in Type 2 Diabetes: Retrospective Case-Control Analysis
Bibliographic record
Abstract
BACKGROUND: Despite guidelines recommending the use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in certain patients with type 2 diabetes (T2D), they are not being prescribed for many of these patients. Web-based continuing medical education (CME) patient simulations have been used to identify clinicians' practice gaps and improve clinical decision-making as measured within a simulation, but the impact of this format on real-world treatment has not been researched. OBJECTIVE: This study aimed to evaluate the effect of a simulation-based CME intervention on real-world use of GLP-1 RAs by endocrinologists and primary care physicians. METHODS: Two evaluation phases of the CME simulation were conducted: phase I, the CME simulation phase, was a paired, pre-post study of 435 physician learners in the United States; and phase II, the real-world phase, was a retrospective, matched case-control study of 157 of the 435 physicians who had claims data available for the study period. RESULTS: Phase I CME results showed a 29 percentage point increase in correct decisions from pre- to postfeedback (178/435, 40.9% to 304/435, 69.9%; P<.001) in selecting treatment that addresses both glycemic control and cardiovascular event protection. Phase II results showed that 39 of 157 (24.8%) physicians in the intervention group increased use of GLP-1 RAs, compared to 20 of 157 (12.7%) in the comparison group. Being in the intervention group predicted GLP-1 RA use after education (odds ratio 4.49; 95% CI 1.45-13.97; P=.001). CONCLUSIONS: A web-based CME simulation focused on secondary prevention of cardiovascular events in a patient with T2D was associated with increased use of evidence-based treatment selection in the real world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".