Pharmacist Interventions to Improve Cardiovascular Disease Risk Factors in Diabetes
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis of randomized controlled trials (RCTs) assesses the effect of pharmacist care on cardiovascular disease (CVD) risk factors among outpatients with diabetes. RESEARCH DESIGN AND METHODS: MEDLINE, EMBASE, CINAHL, and the Cochrane Central Register of Controlled Trials were searched. Pharmacist interventions were classified, and a meta-analysis of mean changes of blood pressure (BP), total cholesterol (TC), LDL cholesterol, HDL cholesterol, and BMI was performed using random-effects models. RESULTS: The meta-analysis included 15 RCTs (9,111 outpatients) in which interventions were conducted exclusively by pharmacists in 8 studies and in collaboration with physicians, nurses, dietitians, or physical therapists in 7 studies. Pharmacist interventions included medication management, educational interventions, feedback to physicians, measurement of CVD risk factors, or patient-reminder systems. Compared with usual care, pharmacist care was associated with significant reductions for systolic BP (12 studies with 1,894 patients; -6.2 mmHg [95% CI -7.8 to -4.6]); diastolic BP (9 studies with 1,496 patients; -4.5 mmHg [-6.2 to -2.8]); TC (8 studies with 1,280 patients; -15.2 mg/dL [-24.7 to -5.7]); LDL cholesterol (9 studies with 8,084 patients; -11.7 mg/dL [-15.8 to -7.6]); and BMI (5 studies with 751 patients; -0.9 kg/m(2) [-1.7 to -0.1]). Pharmacist care was not associated with a significant change in HDL cholesterol (6 studies with 826 patients; 0.2 mg/dL [-1.9 to 2.4]). CONCLUSIONS: This meta-analysis supports pharmacist interventions-alone or in collaboration with other health care professionals-to improve major CVD risk factors among outpatients with diabetes.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".