A systematic review of the effectiveness and complications of fat grafting in the facial region
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to evaluate the safety and efficacy of fat grafting to the facial region for the reconstruction and aesthetic enhancement of facial contour. METHODS: A systematic literature review of the National Library of Medicine (PubMed), MEDLINE and Cochrane databases was performed. Studies involving the outcomes of autologous fat grafting to correct or enhance contour defects of the face were included. Extracted data included patient demographics, harvest and injection sites, graft harvesting and injection technique, mean injected volume, retained volume percentage and complications. RESULTS: Forty-three articles met the inclusion criteria, resulting in 4577 patients with various facial contour defects treated with autologous fat grafting. Injection sites were categorized by anatomic facial regions as upper (32.5%), middle (53%) and lower face (14.5%). The mean volume of injected fat was 16.9 ml. The mean weighted volume retention of non-enriched grafts was 41.63% at the time of follow up (mean 13.9 months). A total of 104 (2.27%) complications were reported including asymmetry (74), skin irregularities (14), prolonged edema (4), graft hypertrophy (4), fat necrosis (3), infection (2), erythema (1), telangiectasia (1), and activation of acne (1). CONCLUSION: The present study represents the first systematic review of fat grafting in the facial region, a widely-performed procedure within plastic surgery practice. Importantly, it presents pooled important data such as retained grafting volume and complication rates in this anatomical region, providing clinicians with more accurate information with which to guide their decision-making and patient education.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.013 |
| 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) | 0.004 | 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".