Breast Cancer Local Recurrence in Patients With and Without Post-Mastectomy Immediate Breast Reconstruction: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Despite rising rates of post-mastectomy immediate breast reconstruction (IBR), there are fears of local recurrence (LR) among patients and physicians. This systematic review and meta-analysis compare long-term LR incidence in patients with mastectomy and IBR (Mast + IBR) to patients with mastectomy alone (Mast − IBR). Methods: Medline, Embase, and Web of Science databases were searched for relevant articles. Articles published between January 2000 and December 2020 were included when LR rates were reported for patients with breast cancer (stage I or II) who underwent mastectomy with or without IBR. A random-effects model was used to calculate pooled odds ratios (ORs) with a 95% confidence interval (CI), adjusted for age, and follow-up time. Results: In total, 1475 unique articles were identified, with 1434 excluded in title abstract screening and 31 excluded in full-text screening. Ten articles, amounting to 15 173 patients (3478 Mast + IBR, 11 695 Mast − IBR), were included. In total, 111 (3.2%) patients in Mast + IBR experienced LR after a mean follow-up time of 72.9 months, while 245 (2.1%) in Mast − IBR experienced LR after a mean follow-up time of 73.3 months. There were no increased odds of LR in Mast + IBR, adjusted for age, and follow-up time (OR 1.17, CI 0.86-1.59, P = 0.59). Conclusion: This meta-analysis supports that IBR is not associated with increased LR odds compared to mastectomy alone. Patients with breast cancer may undergo mastectomy and IBR without concern of increased rates of LR, which contributes to improved quality of life.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".