Does the use of Acellular Dermal Matrices (ADM) in women undergoing pre-pectoral implant-based breast reconstruction increase operative success versus non-use of ADM in the same setting? A systematic review
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common malignancy among women in the UK. Reconstruction - of which implant-based breast reconstruction (IBBR) is the most common - forms a core part of surgical management of breast cancer. More recently, pre-pectoral IBBR has become common as technology and operative techniques have evolved. Many surgeons use acellular dermal matrix (ADM) in reconstruction however there is little evidence in literature that this improves surgical outcomes. This review will assess available evidence for surgical outcomes for breast reconstructions using ADM versus non-use of ADM. METHODS: A database search was performed of Ovid Medline, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews (2012-2022). Studies were screened using inclusion and exclusion criteria. Risk of Bias was assessed using the Newcastle Ottawa scale and ROBIS tools. Analysis and meta-analysis were performed. RESULTS: This review included 22 studies (3822 breast reconstructions). No significant difference between overall complications and failure rates between ADM and non-ADM use was demonstrated. Capsular contracture, wound dehiscence and implant rippling had significant differences however these results demonstrated high heterogeneity thus wider generalisation may be inaccurate. Patient quality of life scores were not recorded consistently or comparably between papers. CONCLUSIONS: This review suggests a lack of significant differences in most complications between ADM use and non-use for pre-pectoral IBBR. If no increase in complications exists between groups, this has significant implications for surgical and legislative decision-making. There is, however, inadequate evidence available on the topic and further research is required.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".