A comprehensive framework and key guideline recommendations for the provision of evidence-based breast cancer survivorship care within the primary care setting
Bibliographic record
Abstract
BACKGROUND: Breast cancer survivors continue to experience physical and psychosocial health care needs post-treatment. Primary care involvement is increasing as cancer centres move forward with earlier discharge of stable breast cancer survivors to primary care follow-up. Research suggests primary care providers (PCPs) are willing to provide survivorship care but many lack knowledge and confidence to provide evidence-based care. Although clinical practice guidelines (CPGs) exist for follow-up surveillance and certain aspects of survivorship care, no single comprehensive guideline addresses all significant breast cancer survivorship issues encountered in primary care. PURPOSE: The purpose of this research was to create a comprehensive clinical practice framework to guide the provision of breast cancer survivorship care in primary care settings. METHODS: This study consisted of an extensive search, appraisal and synthesis of CPGs for post-treatment breast cancer care using a modified Delphi method. Breast cancer survivorship issues and relevant CPGs were mapped to four essential components of survivorship care to create a comprehensive clinical practice framework to guide provision of breast cancer survivorship care. RESULTS: The completed framework consists of a one-page checklist outlining breast cancer survivorship issues relevant to primary care, a three-page summary of key recommendations and a one-page list of guideline sources. The framework and key guideline recommendations were verified by a panel of experts for comprehensiveness, importance and relevance to primary care. CONCLUSIONS: This framework may serve as a tool to remind PCPs about issues impacting breast cancer survivors, as well as the evidence-based recommendations and resources to provide the associated care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".