Efficacy and Safety of Autologous Nanofat Injection in the Treatment of Postburn Scars Using Optical Skin Imaging Analysis
Bibliographic record
Abstract
BACKGROUND: Burn scars are considered one of the challenging issues that can affect the quality of life by causing aesthetic and functional problems. Injecting nanofat particles, which are considered a source of stem cells, into the dermis and/or subcutis of the burned area is considered a promising procedure for the treatment of scars and the correction of volume shortage and skin renewal. OBJECTIVE: To assess the safety and effectiveness of using autologous nanofat injections to treat burn scars. METHODS: Thirty patients with postburn scars participated in the trial. Each patient received one session of liposuction, which was then converted into nanofat and injected back into the scar tissue. Four months after the session, the evaluation was conducted both objectively using the Antera camera 3D imaging and subjectively using the Vancouver scar scale (VSS). RESULTS: Because there were statistically significant improvements in the treated scars' height, color, vascularity, and pliability, the total VSS scores differed significantly before and after treatment. Furthermore, the Antera 3D imaging revealed a statistically significant variation in the treated scars' indentations, erythema, and pigmentation scores. CONCLUSION: The study findings demonstrated that nanofat is a successful postburn scar treatment option that improves patients' quality of life.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".