Impact of Pharmacist Interventions in Patients with Dyslipidemia: A Systematic Review
Bibliographic record
Abstract
Purpose: To determine the effect of pharmacistled interventions in dyslipidemia on clinical and process outcomes. Methods: Search: MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, International Pharmaceutical Abstracts, HealthSTAR, Pascal, MEDLINE In-Process & Other Non-Indexed Citations, CINAHL Plus with Full Text, Health-Source: Nursing Edition, Academic Search Complete, BIOSIS Previews, Science Citation Index Expanded and Social Sciences Citation Index were searched from their inception to September 2008. Where possible an RCT filter was used. Article screening and selection: Inclusion criteria were: 1) RCTs and 2) pharmacist-provided pharmaceutical care, either independently or as part of a health care team or a collaborative agreement (team-directed) with other health care providers. There were no restrictions on language, sample size, study duration or practice setting. Quality assessment: Risk of bias was assessed using the Cochrane Collaboration's Risk of Bias tool and studies were judged as low, high or unclear risk of bias. Data Extraction: Data extraction was performed by 2 independent reviewers using a standardized data collection form. Outcomes: The primary outcome was absolute reduction in LDL cholesterol. Secondary outcomes included proportion of patients at target, initiation/modification of lipid therapy, compliance with lipid therapy, health-related quality of life and patient satisfaction. Data Analysis: Data were analyzed using a random effects model with analysis based on the Der-Simonian-Laird method. Calculations included odds ratio for dichotomous data and weighted mean difference (WMD) or standardized mean differences for continuous data. Subgroup analyses or meta-regression were conducted to investigate possible sources of heterogeneity. An indirect comparison of pharmacist-directed versus pharmacist collaborative care interventions was done. Sensitivity analysis was performed based on risk of bias. Results: A total of 8422 articles were retrieved from the search. From these, 114 articles were selected for full review, and 12 articles were included. The overall difference in LDL ( n = 543 patients) was not statistically significant (WMD −0.09 mmol/L, 95% CI −0.23, 0.04). The difference in total cholesterol was statistically significant (WMD −0.16, 95% CI −0.30, −0.02). Patients followed by a pharmacist were 3 times more likely to be at target (OR 2.9, 95% CI 1.1–7.5) and 2 times more likely to have their cholesterol measured (OR 2.4, 95% CI 1.6–3.6). Conclusions: Pharmacist interventions in a variety of settings have an impact on the lowering of total cholesterol. Patients receiving interventions that included pharmacist care were more likely to be at target and have their lipid panel measured. The types of interventions provided by pharmacists vary in terms of setting and components.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".