How to help patients manage their dyslipidemia: A primary care physician–pharmacist team intervention
Bibliographic record
Abstract
Dyslipidemia treatment in primary care is far from optimal — adherence and persistence to pharmacotherapy are low, and physicians tend not to titrate statin dosages. Consequently, a large proportion of patients do not attain their recommended lipid targets. This has serious clinical and economic consequences. Several studies have shown that community-pharmacist interventions and collaborative management of pharmacotherapy by physicians and pharmacists improve dyslipidemia treatment. In Quebec, as a result of legislative changes (Bill 90) made in 2002, community pharmacists may initiate and adjust drug therapy in accordance with a physician’s prescription and request laboratory analyses when needed. This new legislation increases the potential for a physician-pharmacist team approach to the management of dyslipidemic patients. In Quebec, in order to implement these collaborative practices, a treatment protocol has to be approved by members of a hospital’s Conseil des Medecins, Dentistes et Pharmaciens (Council of Doctors, Dentists, and Pharmacists). In this article, we present a treatment protocol for the management of statin therapy that was developed by pharmacists (LL, JV, DL, MCV, SP), family physicians (MTL, EH), and a cardiologist (JG) as part of a randomized controlled trial. The treatment protocol describes a physician-pharmacist team intervention for the management of patients with dyslipidemia in a primary care setting.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".