Pharmacists as Care Providers for Stroke Patients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Pharmacists have become an integral member of the multidisciplinary team providing clinical patient care in various healthcare settings. Although evidence supporting their role in the care of patients with other disease states is well-established, minimal literature has been published evaluating pharmacist interventions in stroke patients. The purpose of this systematic review is to summarize the evidence evaluating the impact of pharmacist interventions on stroke patient outcomes. METHODS: Study abstracts and full-text articles evaluating the impact of a pharmacist intervention on outcomes in patients with an acute stroke/transient ischemic attack (TIA) or a history of an acute stroke/TIA were identified and a qualitative analysis performed. RESULTS: A total of 20 abstracts and full-text studies were included. The included studies provided evidence supporting pharmacist interventions in multiple settings, including emergency departments, inpatient, outpatient, and community pharmacy settings. In a significant proportion of the studies, pharmacist care was collaborative with other healthcare professionals. Some of the pharmacist interventions included participation in a stroke response team, assessment for thrombolytic use, medication reconciliation, participation in patient rounds, identification and resolution of drug therapy problems, risk-factor reduction, and patient education. Pharmacist involvement was associated with increased use of evidence-based therapies, medication adherence, risk-factor target achievement, and maintenance of health-related quality of life. CONCLUSIONS: Available evidence suggests that a variety of pharmacist interventions can have a positive impact on stroke patient outcomes. Pharmacists should be considered an integral member of the stroke patient care team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".