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 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 | 0.200 | 0.245 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.013 | 0.015 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".