A randomized trial of a community-based approach to dyslipidemia management
Bibliographic record
Abstract
Background: Dyslipidemia is an important risk factor for cardiovascular disease but is suboptimally managed. Pharmacists are accessible primary care professionals and with expanded scopes of practice (including prescribing), could identify and manage patients with dyslipidemia. We sought to evaluate the effect of pharmacist prescribing of dyslipidemia medications on the proportion of participants achieving target LDL-cholesterol (LDL-c) levels. Methods: We conducted a randomized controlled trial in 14 community pharmacies in Alberta, Canada. We enrolled adults with uncontrolled dyslipidemia as defined by the 2009 Canadian Dyslipidemia Guidelines. Intervention was pharmacist-directed dyslipidemia care, including assessment of cardiovascular risk, review of LDL-c, prescribing of medications, health behaviour interventions and follow-up every 6 weeks for 6 months. Usual care patients received their lipid results and a pamphlet on cardiovascular disease and usual care from their physician and pharmacist. Primary outcome was the proportion of participants achieving their target LDL-c (<2 mmol/L or ≥50% reduction) at 6 months between groups. Results: We enrolled 99 patients with a mean (SD) age of 63 (13) years, 49% male and baseline LDL-c of 3.37 mmol/L (0.98). Proportion of patients achieving LDL-c target was 43% intervention versus 18% control ( p = 0.007). Adjusted odds of achieving target LDL-c were 3.3 times higher for the intervention group ( p = 0.031), who also achieved greater reduction in LDL-c (1.12 mmol/L, SE = 0.112) versus control (0.42 mmol/L, SE = 0.109), for an adjusted mean difference of 0.546 mmol/L (SE = 0.157), p < 0.001. Conclusion: Pharmacist prescribing resulted in >3-fold more patients achieving target LDL-c levels. This could have major public health implications.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".