Follow-up after treatment for breast cancer: Practical guide to survivorship care for family physicians.
Bibliographic record
Abstract
OBJECTIVE: To offer FPs a summary of evidence-based recommendations to guide their follow-up survivorship care of women treated for breast cancer. QUALITY OF EVIDENCE: A literature search was conducted in MEDLINE from 2000 to 2016 using the search words breast cancer, survivorship, follow-up care, aftercare, guidelines, and survivorship care plans, with a focus on review of recent guidelines published by national cancer organizations. Evidence ranges from level I to level III. MAIN MESSAGE: Survivorship care involves 4 main tasks: surveillance and screening, management of long-term effects, health promotion, and care coordination. Surveillance for recurrence involves only annual mammography, and screening for other cancers should be done according to population guidelines. Management of the long-term effects of cancer and its treatment addresses common issues of pain, fatigue, lymphedema, distress, and medication side effects, as well as longer-term concerns for cardiac and bone health. Health promotion emphasizes the benefits of active lifestyle change in cancer survivors, with an emphasis on physical activity. Survivorship care is enhanced by the involvement of various health professionals and services, and FPs play an important role in care coordination. CONCLUSION: Family physicians are increasingly the main providers of follow-up care after breast cancer treatment. Breast cancer should be viewed as a chronic medical condition even in women who remain disease free, and patients benefit from the approach afforded other chronic conditions in primary 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.018 |
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".