The Utility of Fat Grafting to Manage Burn Scars: A Systematic Review
Bibliographic record
Abstract
Secondary management of thermal injuries remains a challenging and relevant topic for plastic and nonplastic burn surgeons alike. Burn scars are associated with both functional limitations and aesthetic challenges for patients. While various treatment modalities exist for the management of these scars, no gold standard has been established. Fat grafting has been used in various reconstructive contexts, and studies have demonstrated improvement in skin texture and contour following infiltration. This systematic review aims to examine the available evidence on outcomes following fat grafting for the management of burn scars. A search of Medline, EMBASE, and Cochrane Library databases was conducted from their inception until November 2024. Published articles examining outcomes of fat grafting for thermal injury scars were identified, screened, and data were extracted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 10 740 articles were screened, yielding 14 eligible studies for data extraction, accounting for 885 patients. All studies reported improvement of scars postoperatively. Nine out of 14 studies used a subjective clinical assessment, 1 study did not report pretreatment measurements, and the other 8 studies all found improved outcomes based on clinician assessment. One study reported Vancouver Scar Scale (VSS) scores and another reported modified VSS scores. Three studies utilized POSAS and the mean difference was an improvement of 7.28 (MCID <1). This review suggests that autologous fat grafting and adipose-derived stem cells show promising results for improving scar quality, function, and patient satisfaction following burn injury. Further studies, particularly prospective in nature, with standardized outcome measurements are needed to substantiate subjective clinical improvement. The authors recommend utilizing POSAS or VSS for future studies investigating burn scar treatments.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| 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".