Abstract: Burn Scar Regeneration with the “Sufa” (Subcision and Fat Grafting) Technique. A Prospective Clinical Study
Bibliographic record
Abstract
INTRODUCTION: Treatment of burn scars with traditional surgical techniques is challenging due to recurrent contractures. The use of fat grafting in thermal injury has been previously reported only in small clinical series and results are often biased by simultaneous surgical procedures and lack of scientific methods of validation. 1–5 MATERIALS AND METHODS: Our study prospectively evaluates outcomes in 9 patients treated with the “SUFA” technique (Subcision and Fat Grafting) for debilitating contracted burn scars limiting range of motion. Results are evaluated clinically with the Vancouver scale and by range of motion through the affected joints at 1, 3, 6 and 12 months. Scientific validation of the outcomes is performed evaluating dermal thickening and scar remodeling by high definition ultrasound and histology examination with hematoxylin-eosin and monoclonal antibodies staining. RESULTS: Results show clinical improvement, thickening of dermis and redistribution and reorientation of the collagen fibers within the dermis. Statistical significance (p<00.5) has been obtained for all analyzed data. Fat reabsorption occurred with a mean of 40%. CONCLUSION: Our study gives scientific validation of the efficacy of subcision and fat grafting in contracted scar. New surgical and diagnostic techniques are illustrated. Our clinical and diagnostic outcomes suggest dermis regeneration secondary to the new fat grafting technique DISCLOSURE/FINANCIAL SUPPORT:None of the authors has a financial interest in any of the products, devices, or drugs mentioned in this manuscript. REFERENCES: 1. Brongo S, Nicoletti GF, La Padula S, Mele CM, D’Andrea F. Use of lipofilling for the treatment of severe burn outcomes. Plast Reconstr Surg. 2012 Aug;130(2):374e-376e. 2. Sultan SM, Barr JS, Butala P, Davidson EH, Weinstein AL, Knobel D, Saadeh PB, Warren SM, Coleman SR, Hazen A. Fat grafting accelerates revascularisation and decreases fibrosis following thermal injury. J Plast Reconstr Aesthet Surg. 2012 Feb;65(2):219–27. 3. Viard R, Bouguila J, Voulliaume D, Comparin JP, Dionyssopoulos A, Foyatier JL. Fat grafting in facial burns sequelae. Ann Chir Plast Esthet. 2012 Jun;57(3):217–29. Epub 2011 Jul 30. French 4. Clauser LC, Tieghi R, Galiè M, Carinci F. Structural fat grafting: facial volumetric restoration in complex reconstructive surgery. J Craniofac Surg. 2011 Sep;22(5):1695–701 5. Klinger M, Marazzi M, Vigo D, Torre M. Fat injection for cases of severe burn outcomes: a new perspective of scar remodeling and reduction. Aesthetic Plast Surg. 2008 May;32(3):465–9.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".