Impact of Pharmacist Care in the Management of Cardiovascular Disease Risk Factors
Bibliographic record
Abstract
BACKGROUND: Pharmacists may improve the clinical management of major risk factors for cardiovascular disease (CVD) prevention. A systematic review was conducted to determine the impact of pharmacist care on the management of CVD risk factors among outpatients. METHODS: The MEDLINE, EMBASE, CINAHL, and Cochrane Central Register of Controlled Trials databases were searched for randomized controlled trials that involved pharmacist care interventions among outpatients with CVD risk factors. Two reviewers independently abstracted data and classified pharmacists' interventions. Mean changes in blood pressure, total cholesterol, low-density lipoprotein cholesterol, and proportion of smokers were estimated using random effects models. RESULTS: Thirty randomized controlled trials (11,765 patients) were identified. Pharmacist interventions exclusively conducted by a pharmacist or implemented in collaboration with physicians or nurses included patient educational interventions, patient-reminder systems, measurement of CVD risk factors, medication management and feedback to physician, or educational intervention to health care professionals. Pharmacist care was associated with significant reductions in systolic/diastolic blood pressure (19 studies [10,479 patients]; -8.1 mm Hg [95% confidence interval {CI}, -10.2 to -5.9]/-3.8 mm Hg [95% CI,-5.3 to -2.3]); total cholesterol (9 studies [1121 patients]; -17.4 mg/L [95% CI,-25.5 to -9.2]), low-density lipoprotein cholesterol (7 studies [924 patients]; -13.4 mg/L [95% CI,-23.0 to -3.8]), and a reduction in the risk of smoking (2 studies [196 patients]; relative risk, 0.77 [95% CI, 0.67 to 0.89]). While most studies tended to favor pharmacist care compared with usual care, a substantial heterogeneity was observed. CONCLUSION: Pharmacist-directed care or in collaboration with physicians or nurses improve the management of major CVD risk factors in outpatients.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".