Oncologic outcome of breast reconstruction after mastectomy in breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: We conducted a comprehensive systematic review of the literature and meta-analysis of the oncologic outcomes of breast reconstruction (BR) after mastectomy and mastectomy only. This study aimed to analyze the impact of BR on the prognosis of patients with breast cancer. Methods: A systematic search of MEDLINE and EMBASE databases was performed using the following keywords: breast cancer, mastectomy, and BR. Inclusion criteria were studies reporting the survival data of patients after mastectomy only and mastectomy with BR. Event-free survival (EFS), breast cancer-specific survival (BCSS), and overall survival (OS) were considered the indicators of oncological outcomes. As all the included studies were non-randomized trials, we used the Newcastle-Ottawa Scale (NOS) for risk of bias assessment. The effect of BR on survival was measured using the effect size of the hazard ratio (HR). Data from each study were analyzed using the Review Manager. Results: Fifteen studies with 20,948 cases of BR and 63,358 cases of mastectomy were included. The pooled HRs for EFS and BCSS were 1.07 [95% confidence interval (CI): 0.78-1.47, P=0.65] and 0.84 (95% CI: 0.64-1.11, P=0.22), respectively. The patients who underwent BR after mastectomy had similar EFS and BCSS scores. Furthermore, patients who received BR had better OS (HR =0.73; 95% CI: 0.61-0.88, P=0.001) than those who underwent mastectomy only. Conclusions: The data showed that BR after mastectomy had similar EFS and BCSS and better OS than mastectomy alone. Our meta-analysis suggests that BR is a practicable and safe treatment option for patients with breast cancer.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".