Effect of Nano Fat Graft on the Healing of Donor Site of Split Thickness Skin Graft
Bibliographic record
Abstract
Background: Split-thickness grafts (STSGs) harvestingis a common technique to reconstruct absent skin and to reestablishthe skin barrier in burn and skin defects. Donor sitemorbidity is not uncommon. Various materials and dressingshad been used to improve healing of donor site. Fat graftingor its components had been used in promotion of healing inchronic and irradiated wounds indicating the possibility ofimproving healing.Objective: To evaluate the effect of adding nano fat graftsto donor sites of STSGs.Patients and Methods: Twenty (20) adult patients wereincluded in this comparative self-controlled clinical trial fromMarch 2020 to April 2021. These patients had raw areas ofskin needing STSG. STSG thickness was 0.12.5 inch leavingan area measuring 7x15cm (105cm2) at the donor site. Thedonor site area in the lower limb was used as a test area whereit was divided into two equal areas, one area was covered bynano fat graft and Vaseline gauze (group A), the other actingas a control group was covered by Vaseline gauze only (group < br />B). Group A was compared to group B as regard healing timefrom 10th day onward and quality of healing after one monthusing Vancouver scale (VSS). Tissue biopsy was taken at day21 from both groups. Any Donor site complications werenoted.Results: Comparing The rate of donor site healing ingroup A with group B, group A showed faster healing with amean of (13.30±2.61) versus (16.05±2.43) days from the dateof harvesting of STSG. Histologically the mean thickness ofneo epithelium in group A was more than that of group B witha mean value of 255.04±15.27mm and 161.15±28.75mm consecutively.Group A showed better vascularity and pliabilitywhile no difference was detected as regard pigmentation andheight. Infection occurred in donor site of one patient.Conclusions: Using topical nano fat graft and Vaselinegauze on the donor site of STSG improves healing time aswell as vascularity and pliability in comparison to Vaselinegauze alone.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".