Effect of Adding Pharmacists to Primary Care Teams on Blood Pressure Control in Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of adding pharmacists to primary care teams on the management of hypertension and other cardiovascular risk factors in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: We conducted a randomized controlled trial with blinded ascertainment of outcomes within primary care clinics in Edmonton, Canada. Pharmacists performed medication assessments and limited history and physical examinations and provided guideline-concordant recommendations to optimize medication management. Follow-up contact was completed as necessary. Control patients received usual care. The primary outcome was a ≥10% decrease in systolic blood pressure at 1 year. RESULTS: A total of 260 patients were enrolled, 57% were women, the mean age was 59 years, diabetes duration was 6 years, and blood pressure was 129/74 mmHg. Forty-eight of 131 (37%) intervention patients and 30 of 129 (23%) control patients achieved the primary outcome (odds ratio 1.9 [95% CI 1.1-3.3]; P = 0.02). Among 153 patients with inadequately controlled hypertension at baseline, intervention patients (n = 82) were significantly more likely than control patients (n = 71) to achieve the primary outcome (41 [50%] vs. 20 [28%]; 2.6 [1.3-5.0]; P = 0.007) and recommended blood pressure targets (44 [54%] vs. 21 [30%]; 2.8 [1.4-5.4]; P = 0.003). The 10-year risk of cardiovascular disease, based on changes to the UK Prospective Diabetes Study Risk Engine, were predicted to decrease by 3% for intervention patients and 1% for control patients (P = 0.005). CONCLUSIONS: Significantly more patients with type 2 diabetes achieved better blood pressure control when pharmacists were added to primary care teams, which suggests that pharmacists can make important contributions to the primary care of these patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".