Fat Grafting for Gluteal Augmentation: A Systematic Review of the Literature and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: With the increasing demand for gluteal fat augmentation, reports of fatal complications have surfaced. Therefore, the authors proposed to analyze the published techniques and compare different protocols, to identify those of potential concern. METHODS: A systematic review of the literature was performed with a search of 21 terms on the PubMed, MEDLINE, Cochrane, and Scientific Electronic Library Online databases. Nineteen articles meeting our predetermined criteria were analyzed, and data from the different steps of the procedure were classified, allowing evaluation and comparison of techniques. Independent-samples t test and one-way analysis of variance were used for statistical analysis. RESULTS: Seventeen case series and two retrospective studies including 4105 patients were reviewed. Most articles were authored in Colombia, Mexico, and Brazil. Most procedures were performed on adult female patients under general anesthesia. Fat was harvested using a tumescent technique from the lower extremities and the back, with machine-vacuum suction. A mean of 400 ml of decanted lipoaspirate was injected into each gluteal region, mostly subcutaneously and intramuscularly with 60-ml syringes. Most patients rated their results as "excellent." The mean complication rate was 7 percent (6.7 percent minor, 0.32 percent major), with no significant relation to the planes of injection. CONCLUSIONS: Fat grafting is an effective and predictable way to remodel the gluteal region; however, the procedure is not without risks. Avoiding gluteal vessel damage may prevent most feared complications, such as fat embolism. Accurate analysis, systematization of the procedure, and reporting cases in the fat grafting registry may provide the foundation for optimization of outcomes. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.008 | 0.009 |
| 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".