Current Applications and Indications of Allograft Adipose Matrix: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Autologous fat transfer remains a staple in aesthetic and reconstructive surgery because of its regenerative capacity and tissue integration. However, limitations such as donor-site morbidity and unpredictable volume retention persist. Allograft adipose matrix (AAM), a product made from human fat tissue that serves as a natural scaffold to promote adipogenesis, offers a promising alternative, serving as a scaffold for adipogenesis and delivering key extracellular matrix components. This review examines clinical applications and outcomes in soft-tissue augmentation. METHODS: A systematic review was conducted by means of PubMed/MEDLINE/Cochrane/Embase using "allograft adipose matrix," "decellularized adipose matrix," "Renuva," and "Leneva." Studies published up to May 30, 2024, involving human or animal AAM treatment, were included. Data on demographics, clinical use, injection protocols, outcomes, volume retention, and complications were extracted and analyzed. RESULTS: From 352 studies, 10 involving humans and 9 involving animals were included. Human studies included 93 patients. Indications for AAM included foot and dorsal hand rejuvenation, abdominal and buttocks contouring, temple atrophy, breast and genitalia augmentation, pressure ulcers, and facial rejuvenation. AAM injection volumes varied, with retention rates ranging from 21.5% to 100%. The most common complications were erythema, swelling, injection-site pain, and burning, all resolving easily. Patient satisfaction scores ranged from 72.9% to 100%. CONCLUSIONS: AAM provides a promising biomaterial for soft-tissue augmentation. Histologic analysis supports its role in adipogenesis and neovascularization. Animal studies suggest enhancements through combination therapies, including AAM with autologous fat, platelet-rich plasma, or synthetic scaffolds. Further research is needed to optimize decellularization protocols and improve bioactivity and tissue incorporation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".