Breast Reconstruction in De Novo Metastatic Breast Cancer: A Systematic Review
Bibliographic record
Abstract
Background: Breast reconstruction in de novo metastatic breast cancer (dnMBC) patients is a viable option. There remains no consensus on recommendations. We summarize postreconstruction clinical outcomes in dnMBC patients to identify surgical candidates. Methods: A systematic review was conducted across PubMed/MEDLINE, Scopus, and Web of Science from January 1, 1990, to November 1, 2024. The study methods were in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data on patient demographics, disease characteristics, oncological treatment, surgical details, and clinical outcomes were collected. Results: A total of 7 studies (2635 breast cancer survivors) were identified. The average (SD) age was 47.5 (2.35) years, and most participants were White (n = 2080, 79.3%). Across studies, 39.0% (n = 761) of patients underwent implant-based reconstruction, 38.8% (n = 757) autologous reconstruction, 5.99% (n = 117) combined reconstruction, and 16.4% (n = 320) were not specified. Most cancers were invasive ductal carcinoma (81.3%) with estrogen-positive (73.1%) or progesterone-positive (48.4%) receptors and human epidermal growth factor receptor 2-positive (33.7%) status. Primary tumors most often metastasized to bone (44.4%) or lymph nodes (38.5%). Overall survival and breast cancer-specific survival rates were prolonged among reconstructed patients without increased predilection for complications or delay in tumor treatment. Conclusions: Reconstruction in dnMBC patients is an appropriate option, especially among younger patients with oligometastatic disease. Future studies are encouraged to investigate the impact on well-being and prolonged survival rates, which primarily seem to be limited to those with low disease burden and hormone receptor-positive tumor subtypes.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".