Body image in older breast cancer survivors: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Breast cancer is the most common cancer among women world-wide. In North America survival rates are >80%, resulting in a large population of survivors. The goal of this review was to systematically explore the literature to identify the status of body image and factors that can impact the body image of older breast cancer survivors. METHODS: A systematic review of the literature was conducted and registered with PROSPERO (CRD42019133617). EMBASE and PubMed were searched for articles including terms related to "body image" and "breast cancer." Duplicates were removed and the remaining 322 abstracts were screened. Articles published before 2000, were off-topic, or those that were non-primary research articles were excluded. Sixty-nine remaining full-length articles were screened for language, gender and location. Seven articles underwent quality assessment of which five passed and were reviewed in depth. The remaining two articles were briefly discussed. RESULTS: The literature review suggests that body image is considered important in older BCS and that body image may impact or be impacted by several factors including age, menopausal status, mental health, treatment modality and exercise. Additionally, themes of dealing with physical changes and the length of time women are impacted following treatment were explored. CONCLUSION: Our findings highlight that older women may be at an advantage in terms of being post-menopausal, however concerns surrounding physical and emotional changes affecting body image are indeed present. Future studies on breast cancer survivorship should consider the inclusion of body image as an outcome measure in addition to including individuals representing a wide range of ages.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".