Effects of Nanofat in Plastic and Reconstructive Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Since nanofat was first introduced by Tonnard in 2013, numerous studies have reported positive findings with its use; however, concerns exist regarding its effects and mechanisms, and the various methods used to generate nanofat also remain unclear. The authors conducted a systematic review to evaluate the efficacy of nanofat grafting alone in plastic and reconstructive surgery. METHODS: The MEDLINE, Embase, Cochrane Central, Web of Science, and Scopus databases were searched for studies related to the use of nanofat grafting alone in plastic and reconstructive surgery. Outcomes of interest were all clinical results in humans or animals. RESULTS: Twelve studies were included. No meta-analysis was conducted due to the clinical heterogeneity of the studies. In general, included studies had a low level of evidence. Six studies ( n = 253 patients) showed significant improvements in scar characteristics based on Patient and Observer Scar Assessment Scale, FACE-Q scale, physician assessment, patient satisfaction, and Vancouver Scar Scale scores. Four studies described the benefits of nanofat in skin rejuvenation (wrinkles, fine rhytides, pigmentation, and discoloration) through photographs, questionnaires, and indentation indices. Histologic evaluation illustrated overall increases in skin thickness, collagen, and elastic fibers. Three experimental studies showed the beneficial effects of nanofat on fat grafting, diabetic wound healing, and hair growth, with compelling histological evidence. No severe complication was reported. CONCLUSIONS: Nanofat grafting shows potential benefits in scar and antiaging treatments, with conclusive histological evidence. Clinical studies of fat grafting, wound healing, and hair growth should be conducted, based on the results of this systematic review. Nanofat grafting could be a practical and safe procedure.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".