Effectiveness of an academic detailing service to support appropriate prescribing and care in patients with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Academic detailing (AD), a one-on-one evidence-based educational outreach strategy for healthcare providers, has been effective in improving prescribing behavior. However, its impact on diabetes care outcomes in Canada remains underexplored. OBJECTIVE: We aimed to compare prescribing and care patterns for type 2 diabetes between physicians who received AD and those who did not. METHODS: We conducted a population-based matched cohort study in Ontario, Canada, using health administrative databases. We included primary care physicians with active billing from September 2020 to September 2022. Each AD physician was matched to a maximum four controls based on index year, region, sex, years in practice, and proportion of patients with diabetes. We assessed monthly clinical outcomes for 12 months pre and 18 months postintervention using mixed-effects models. RESULTS: The cohort included 372 AD and 1450 control physicians, with balanced demographics. At baseline, AD physicians saw fewer patients (1292 vs. 1526) but delivered more appointments per patient (4.2 vs. 3.0). Both groups had 15% of patients with diabetes. Post-intervention, biosimilar insulin use increased more sharply in the AD group (9.0% vs. 5.6% monthly). AD physicians consistently had higher B12 testing among metformin users (76.5% vs. 60.0%) and greater use of SGLT2 inhibitors or GLP-1 receptor agonists (40.1% vs. 31.5%). A1C control (<8%) remained similar across groups (∼80%). Time × group differences were significant for all outcomes (P < 0.001) except B12 testing (P = 0.790) and A1C levels (P = 0.815). CONCLUSIONS: The AD group saw greater improvements in diabetes prescribing post-intervention. Engaging physicians in AD could enhance diabetes care quality.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".