Comparative study between nanofat injection with and without Platelet-Rich Plasma (PRP) in improvement of mature scar: clinical study
Bibliographic record
Abstract
and poor suppleness. Regenerative therapy include nanofat, rich in stromal vascular fraction, and PRP, a concentration of autologous platelets releasing growth factors. The combination has been hypothesized to improve scar remodeling Objectives: To compare nanofat injection with and without Platelet-Rich Plasma(PRP) in improvement of mature scar. Patients and methods: This prospective, split-scar study at Qena University Hospital(August 2024–2025) included 15 adults with facial scars(>6 months old). The study was ethically approved under SVU-MED-SUR011-1-24-9-947. Each patient received nanofat on one half of the scar and nanofat+PRP on the other. Fat was harvested mainly from the abdomen, emulsified and filtered into nanofat; PRP was obtained by double centrifugation. Injections were superficial or subdermal, followed by standard wound care and medications. Scars were evaluated at 1, 3, and 6 months using Patient and Observer Scar Assessment Scale (POSAS), Vancouver Scar Scale (VSS), and pain scores. Results: The nanofat + PRP group showed significantly lower POSAS scores at 1(34.67±4.71 vs. 41.87±6.28; p=0.0019), 3(25.2±3.66 vs. 31.27±6.48; p=0.0049), and 6 months(17.4±2.68 vs. 22.6±4.67; p=0.0012). VAS pain scores were also significantly lower at 3(3.6±0.95 vs. 5±0.89; p=0.0004) and 6 months(1.8±0.75 vs. 2.93±0.85; p=0.0009). VSS scores improved significantly at 3(p < 0.0001) and 6 months(p=0.0438). Patient satisfaction was significantly higher with the combination therapy(p=0.0086). Conclusion: Nanofat with PRP improves mature scar look, discomfort, and patient satisfaction more than nanofat alone without increasing side effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".