Breast cancer survivorship care: a narrative review of challenges and future directions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Breast cancer (BC) is the most prevalent cancer among women worldwide. With a growing number of BC survivors (BCSs), the number of survivors who require high-quality survivorship care is increasing. Various recommendations have been proposed for survivorship care plans (SCPs). However, globally, limited progress has been made to implement these recommendations consistently in cancer care centers. This review explores the gaps and challenges that exist in BC survivorship care (BCSC) and proposes future directions for improving survivorship care for patients and the healthcare system. METHODS: Current literature on BCSC was searched using PubMed and Google Scholar. The search strategy utilized a combination of keywords related to BCSC, gaps in survivorship care, and health promotion. Retrievable and English articles from January 2000 to March 2024 were included in the review. KEY CONTENT AND FINDINGS: Despite the large number of guidelines and recommendations on best BCSC practices, only a small number of these have been translated into clinical practices that help streamline patient care. There are many gaps to the provision of high-quality survivorship care, all of which negatively affect patient outcomes. Some of these gaps include but are not limited to: the limited role of primary care providers (PCPs), lack of coordination of care, lack of evidence-based research, insufficient data on health promotion, and challenges implementing comprehensive care. CONCLUSIONS: These findings indicate the need for a holistic and personalized approach to BCSC. The importance of implementing a multi-disciplinary and coordinated approach to survivorship care has been emphasized. This includes further involvement of PCPs, through increased training for PCPs in survivorship care. Despite available models of survivorship care, further research is needed to determine optimal BCSC that improves patient outcomes while decreasing the strain on the healthcare system. Additionally, technology can play a beneficial role in survivorship care, especially through telehealth and artificial intelligence (AI). Nonetheless, further research is needed on BCSC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".