The effect of pharmacist intervention and patient education on lipid-lowering medication compliance and plasma cholesterol levels.
Bibliographic record
Abstract
BACKGROUND: Dyslipidemias are a modifiable risk factor for coronary heart disease. The benefits of cholesterol reduction drug therapies are limited by poor patient compliance with drug regimens. OBJECTIVES: To determine the impact of a community pharmacist pilot disease-management program on patient compliance with lipid-lowering drug therapy and on serum cholesterol levels. METHODS: One hundred forty-nine patients who were nonadherent to prescribed hypolipidemic drug regimens were recruited for this six-month prospective study. Each subject served as their own control. Pharmacists educated these patients on lipid disorders, the benefit of medication compliance and lifestyle modifications that reduce the risk for coronary heart disease. Pharmacists followed up participants by telephone at two-month intervals. Drug renewal rates were monitored throughout the study and plasma lipid levels were measured at study outset and study end. RESULTS: Pharmacist intervention and patient-education programs significantly increased medication compliance, as shown by a 15.3% increase (P<0.05) in the number of compliant patients and an 11 day (P<0.001) reduction in the average number of days to prescription renewal. Concurrently, levels of total cholesterol, triglycerides and low-density lipoprotein (LDL) cholesterol, were reduced by 6%, 16.2%, and 8.5% (P<0.001, 0.01, 0.01), respectively. High density lipoprotein (HDL) cholesterol remained relatively unchanged (+0.7%) so that the LDL to HDL ratio was improved by 17.2% overall (P<0.01). Almost all of the patients (99.2%) were satisfied with the program and expressed a willingness to pay an average $34.50 per 30 min consultation for the pharmacist services offered. CONCLUSION: Pharmacists can contribute significantly to disease management of dyslipidemic individuals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".