The Impact of Adjuvant Radiotherapy on Immediate Implant-based Breast Reconstruction Surgical and Satisfaction Outcomes: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Adjuvant radiotherapy could be a necessary step in the oncological treatment for breast cancer. However, radiotherapy may have negative effects on implant-based immediate breast reconstruction. The aim of this study was to determine the impact of adjuvant radiation therapy on surgical results and patient-reported satisfaction outcomes in women undergoing immediate implant-based breast reconstruction. METHODS: A systematic search in PubMed was conducted on September 2019 and updated on April 2021. The risk of bias of the included studies was assessed using the Newcastle-Ottawa Quality Assessment Form for Observational Studies. RevMan 5 was used for statistical analysis. We obtained relative risks to determine the complication incidence and mean differences for 2-year BREAST-Q scores. RESULTS: Fourteen studies were included. A total of 11,958 implant-based immediate reconstructions were performed, 2311 received postmastectomy radiation therapy, and 9647 were considered as control group. Surgical complications, reoperation rates, and reconstruction failure were significantly higher among irradiated breasts. Significantly lower BREAST-Q scores were reported by irradiated women receiving radiotherapy. CONCLUSIONS: This systematic review and meta-analysis combines reconstruction complication rates with aesthetic and patient-reported satisfaction outcomes. Adjuvant radiotherapy is consistently associated with greater complication rates and poorer aesthetic and satisfaction outcomes. The magnitude of association is significantly lower when the reconstruction is based on autologous tissues.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 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".