Optimizing Adjuvant Care in Early Breast Cancer: Multidisciplinary Strategies and Innovative Models from Canadian Centers
Bibliographic record
Abstract
The adjuvant treatment landscape for hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early breast cancer (EBC) is rapidly evolving, with a diverse range of therapeutic options-including endocrine therapies, bisphosphonates, ovarian function suppression, olaparib, CDK4/6 inhibitors, and emerging agents such as immunotherapy. While these advances have markedly improved patient outcomes, they also introduce challenges related to implementation, monitoring, and resource allocation. Notably, therapies like CDK4/6 inhibitors require particularly close monitoring, creating logistical and capacity challenges for medical oncologists, whose workloads are already stretched due to rising cancer incidence and treatment complexities. These challenges underscore the need for innovative care delivery solutions to ensure patients with EBC continue to receive optimal care. This paper offers a comprehensive guide-a playbook-of multidisciplinary-team-based care models designed to optimize adjuvant treatment delivery in EBC. Drawing on real-world evidence and successful applications across Canadian centers, we explore models led by nurses, nurse practitioners (NPs), general practitioners in oncology (GPO), and pharmacists. Each model leverages the unique expertise of its team to manage treatment toxicities, facilitate adherence, and enhance patient education, thereby promoting effective and sustainable care delivery. Importantly, these models are not intended to compete with one another, but rather to serve as a flexible recipe book from which breast cancer care teams can draw strategies tailored to their local resources and patient needs. By detailing implementation strategies, benefits, and challenges-in many instances supported by quantitative metrics and economic evaluations-this work aims to inspire care teams nationwide to optimize the adjuvant management of patients with HR+, HER2- EBC.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".