Outcome of Quality of Life for Women Undergoing Autologous versus Alloplastic Breast Reconstruction following Mastectomy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: This review aimed to meta-analyze the quality of life of alloplastic versus autologous breast reconstruction, when measured with the BREAST-Q. METHODS: An electronic PubMed and EMBASE search was designed to find articles that compared alloplastic versus autologous breast reconstruction using the BREAST-Q. Studies that failed to present BREAST-Q scores and studies that did not compare alloplastic versus autologous breast reconstruction were excluded. Two authors independently extracted data from the included studies. A standardized data collection form was used. Quality was assessed using the Newcastle-Ottawa Scale. The mean difference and 95 percent confidence intervals between breast reconstruction means were estimated for each BREAST-Q subscale. Forest plots and the I statistic were used to assess heterogeneity and funnel plot publication bias. The Z test was used to assess overall effects. RESULTS: Two hundred eighty abstracts were found; 10 articles were included. Autologous breast reconstruction scored significantly higher in the five subscales than alloplastic breast reconstruction. The Satisfaction with Breasts subscale indicated the greatest difference, with a mean difference of 6.41 (95 percent CI, 3.58 to 9.24; I = 70 percent). The Satisfaction with Results subscale displayed a mean difference of 5.52. The Sexual Well-Being subscale displayed a mean difference of 3.85. The Psychosocial Well-Being subscale displayed a mean difference of 2.64. The overall difference in physical well-being was significant, with high heterogeneity (mean difference, 3.33; 95 percent CI, 0.18 to 6.48; I = 85). CONCLUSION: Autologous breast reconstruction had superior outcomes compared with alloplastic breast reconstruction as measured by the BREAST-Q.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".