Impact of the Diabetes Canada Guideline Dissemination Strategy on the Prescription of Vascular Protective Medications: A Retrospective Cohort Study, 2010–2015
Bibliographic record
Abstract
OBJECTIVE The 2013 Diabetes Canada guidelines launched targeted dissemination tools and a simple assessment for vascular protection. We aimed to 1) examine changes associated with the launch of the 2013 guidelines and additional dissemination efforts in the rates of vascular protective medications prescribed in primary care for older patients with diabetes and 2) examine differences in the rates of prescriptions of vascular protective medications by patient and provider characteristics. RESEARCH DESIGN AND METHODS The study population included patients (≥40 years of age) from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) with type 2 diabetes and at least one clinic visit from April 2010 to December 2015. An interrupted time series analysis was used to assess the proportion of eligible patients prescribed a statin, ACE inhibitor (ACEI)/angiotensin receptor blocker (ARB), or antiplatelet prescription in each quarter. Proton pump inhibitor (PPI) prescriptions were the reference control. RESULTS A dynamic cohort was used where participants were enrolled each quarter using a prespecified set of conditions (range 25,985–70,693 per quarter). There were no significant changes in statin (P = 0.43), ACEI/ARB (P = 0.42), antiplatelet (P = 0.39), or PPI (P = 0.16) prescriptions at baseline (guideline intervention). After guideline publication, there was a significant change in slope for statin (−0.52% per quarter, SE 0.15, P < 0.05), ACEI/ARB (−0.38% per quarter, SE 0.13, P < 0.05), and reference PPI (−0.18% per quarter, SE 0.05, P < 0.05) prescriptions. CONCLUSIONS There was a decrease in prescribing trends over time that was not specific to vascular protective medications. More effective knowledge translation strategies are needed to improve vascular protection in diabetes in order for patients to receive the most effective interventions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".