Adding Pharmacists to Primary Care Teams Increases Guideline-Concordant Antiplatelet Use in Patients with Type 2 Diabetes: Results from a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Antiplatelet therapy is recommended as part of a strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. However, compliance with these guideline-recommended therapies appears to be less than ideal. OBJECTIVE: To assess the effect of adding pharmacists to primary care teams on initiation of guideline-concordant antiplatelet therapy in type 2 diabetic patients. METHODS: Prespecified secondary analysis of randomized trial data. In the main study, the pharmacist intervention included a complete medication history, limited physical examination, provision of guideline-concordant recommendations to the physician to optimize drug therapy, and 1-year follow-up. Controls received usual care without pharmacist interactions. Patients with an indication for antiplatelet therapy, but not using an antiplatelet drug at randomization were included in this substudy. The primary outcome was the proportion of patients using an antiplatelet drug at 1 year. RESULTS: At randomization, 257 of 260 study patients had guideline-concordant indications for antiplatelet therapy, but less than half (121; 47%) were using an antiplatelet drug. Overall, 136 patients met inclusion criteria for the substudy (71 intervention and 65 controls): 60% were women, with mean (SD) age 58.0 (11.9) years, diabetes duration 5.3 (6.0) years, and hemoglobin A(1c) 7.6% (1.5). Sixteen (12%) had established cardiovascular disease at enrollment. At 1 year, 43 (61%) intervention patients and 15 (23%) controls were using an antiplatelet drug (38% absolute difference; number needed to treat, 3; relative increase, 2.6; 95% CI 1.5-4.7; p < 0.001). Of these 58 patients, 52 (90%) were using aspirin 81 mg daily. CONCLUSIONS: Adding pharmacists to primary care teams significantly and substantially increased the proportion of type 2 diabetic patients using guideline-concordant antiplatelet therapy.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".