Integration of a Pharmacist into a Stroke Prevention Clinic Team
Bibliographic record
Abstract
INTRODUCTION Stroke is the fourth leading cause of death in Canada, accounting for 14 000 deaths annually. Between 40 000 and 50 000 strokes occur every year, 75% of which result in some type of impairment or disability.1 Stroke survivors have a 20% risk of another stroke within 2 years of the initial event, and 33% of all strokes are thought to be repeat episodes.1,2 The use of antiplatelet agents and the management of risk factors, such as smoking, diabetes, atrial fibrillation, physical inactivity, excessive alcohol intake, hypertension, and dyslipidemias, are key to preventing recurrent stroke.2,3 Although numerous studies have demonstrated that patient outcomes improve when pharmacists are involved in cardiovascular risk reduction and anticoagulation management, few publications have outlined pharmacists’ involvement in secondary stroke prevention.4 The purposes of this paper are to describe the rationale for pharmacist involvement in a stroke prevention clinic, to outline the role of the pharmacist in the clinic, and to retrospectively evaluate the pharmacist’s workload, to determine the number and nature of the patient care interventions performed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".