Comprehensive Geriatric Assessment for Older Women with Early-Stage (Non-Metastatic) Breast Cancer—An Updated Systematic Review of the Literature
Bibliographic record
Abstract
BACKGROUND: A previous systematic review by our team (2012) undertook comprehensive geriatric assessment (CGA) in breast cancer and concluded there was not sufficient evidence to instate CGA as mandatory practice. SIOG/EUSOMA guidelines published in 2021 advocate the use of CGA in breast cancer patients. The aim is to perform an updated systematic review of the literature. METHODS: A systematic review of studies published between 2012 and 2022 that assessed the use of CGA in breast cancer was performed on Cochrane, PubMed and Embase. RESULTS: A total of 18 articles including 4734 patients with breast cancer were identified. The studies covered four themes for use of CGA in breast cancer: (1) to determine factors influencing survival (2) as an adjunct to treatment decision-making (3) to measure quality of life, and (4) to determine which tools should be included. There was evidence to support the use of CGA in themes 1-3; however, it is uncertain which assessment tools are best to use (theme 4). CONCLUSIONS: CGA can be used to determine factors affecting survival and quality of life in breast cancer patients and can therefore be used to aid treatment decision-making. Further work is required to determine gold standard CGA.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".