Implementation of Pharmacist Case-Finding and Care Pathway Intervention for Vascular Prevention (PRxOACT): Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Cardiovascular (CV) disease (CVD) is the leading cause of death worldwide. The risk factors contributing to CVD development have been well known for decades, but treatment gaps persist. Pharmacists are frontline primary healthcare providers whose interventions to lower CV risk are supported by rigorous evidence. However, efforts to support the widespread implementation of pharmacist interventions to reduce CV risk are needed. To support such implementation, we developed an electronic tool (the "Care Pathway") for guideline-directed assessment, prescription, and follow-up for CVD risk reduction that incorporates shared decision-making. The aim of this trial is to determine the impact of the pharmacist-led Care Pathway intervention on participants' estimated risk for major CV events. This investigator-initiated, multicentre, open-label, randomized controlled trial will include 982 patients (aged ≥ 18 years) with ≥ 1 risk factor for CVD. Patients will be randomized in a 1:1 ratio to receive either a pharmacist-led Care Pathway intervention or usual care. Participants' estimated CV risk will be calculated at baseline and at a 6-month follow-up evaluation. The primary outcome is the difference in change in estimated CV risk from baseline to the 6-month follow-up evaluation between the groups. Pharmacist-led assessment and management of patients' CV risk factors may serve as an effective intervention to reduce patients' estimated risk for major CV events. Formal evaluation of widespread implementation of a Care Pathway intervention will be conducted for the first time in a pharmacy practice CV risk-reduction trial. Clinical Trial Registration: The University of Alberta Human Research Ethics Board (Pro00139142). NCT06405880.
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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 | 0.049 | 0.047 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.090 | 0.016 |
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".