Chest Wall Resection for Recurrent Breast Cancer in the Modern Era
Bibliographic record
Abstract
OBJECTIVE: To review the literature on chest wall resection for recurrent breast cancer and evaluate overall survival (OS) and quality-of-life (QOL) outcomes. BACKGROUND: Full-thickness chest wall resection for recurrent breast cancer is controversial, as historically these recurrences have been thought of as a harbinger of systemic disease. METHODS: A systematic search in MEDLINE, EMBASE, and Cochrane CENTRAL identified 48 eligible studies, all retrospective, accounting for 1305 patients. The review is reported following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Primary end points were patient-centered QOL outcomes and OS; secondary outcomes included disease-free survival (DFS) and 30-day morbidity. Risk of bias was assessed using the Methodological Index for Non-Randomized Studies instrument and the Oxford Centre for Evidence-Based Medicine's levels of evidence tool. Random-effects meta-analysis was used to create pooled estimates. Meta-regressions and sensitivity analyses were used to explore study heterogeneity by age, year of publication, risk of bias, and surgical intent (curative vs palliative). RESULTS: Studies consistently reported excellent OS and DFS in properly selected patients. Pooled estimates for 5-year OS in all studies and those from the past 15 years were 40.8% [95% confidence interval (CI) 35.2-46.7) and 43.1% (95% CI 35.8-50.7), whereas pooled 5-year DFS was 27.1% (95% CI 16.6-41.0). Eight studies reported excellent outcomes related to QOL. Mortality was consistently low (<1%) and 30-day pooled morbidity was 20.2% (95% CI 15.3%-26.3%). Study quality varied, and risk of selection bias in included studies was high. CONCLUSIONS: Full-thickness chest wall resection can be performed with excellent survival and low morbidity. Few studies report on QOL; prospective studies should focus on patient-centered outcomes in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".