The Role of Pharmacists in Delivering Pharmaceutical Services to Breast Cancer Patients in Clinical and Community Settings: A Scoping Review
Bibliographic record
Abstract
(1) Background: Patient-centered care for individuals with breast cancer requires multidisciplinary cooperation to ensure the appropriate use of medication and prevent medication-related problems. Pharmaceutical care has been associated with improved adherence in breast cancer management, a factor linked to patient outcomes and mortality. This study aims to summarize and explore the provision and utilization of pharmaceutical services for breast cancer patients by pharmacists. (2) Methods: A scoping review was performed to assess the pharmacist's role in providing pharmaceutical services for patients with breast cancer. A comprehensive review of four databases (PubMed, Ovid Embase, Ovid International Pharmaceutical Abstracts, and Scopus) was completed between 1 January 2012 and 8 April 2025 according to PRISMA-ScR framework. (3) Results: A total of 46 articles met the inclusion criteria, which included RCTs, observatory studies, cohort studies, and reviews. Findings suggest that both clinical and community pharmacists play an important role in prevention, management, and education for breast cancer patients. (4) Conclusions: Pharmacists can improve health outcomes by providing pharmaceutical service in breast cancer care. Optimizing interventions, expanding services, and evaluating long-term cost-effectiveness is needed in the future.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".