Clinical Pharmacist-Led Interventions for Improving Breast Cancer Management—A Scoping Review
Bibliographic record
Abstract
Breast cancer is the leading cause of cancer-related death in women worldwide and the fifth most common cause of cancer death overall. Most women with breast cancer have a good prognosis if the cancer is detected at an early stage and the patients have access to the appropriate treatment and disease management. This study aims to evaluate the impact of pharmacist-led interventions on breast cancer management and health outcomes. A literature review was carried out through the scientific databases PubMed, Scopus, and Web of Science using predefined keywords. Only full-text original articles written in English that investigated the role of the pharmacist in the management of breast cancer were included in the final analysis. No publication date limits were set. A total of 1625 articles were retrieved from the electronic databases, of which 14 met the inclusion criteria. The current scoping review consists of different study types, including randomized controlled trials, cross-sectional studies, pre-post studies, retrospective cohort studies, quality improvement projects, case-control studies, and one pharmacoeconomic study. Pharmacists commonly provided the following interventions: consultations regarding chemotherapy treatment, risk assessment and patient education, adverse drug reactions and drug-drug interactions detection, and adherence assessment. This scoping review highlights the beneficial effects of the involvement of pharmacists in breast cancer management, such as better quality of life, reduced drug interaction risk, greater adherence rates, and improved patient knowledge. This confirms the importance of including the pharmacist in the oncology team caring for patients with breast cancer.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".