Autologous nanofat injection in treatment of scars: A clinico‐histopathological study
Bibliographic record
Abstract
BACKGROUND: Scars are the unfortunate outcome of most injuries and some diseases. Its psychological impact on patients can deeply affect their quality of life. AIM: The aim of this study was to evaluate the efficacy of autologous nanofat injection in improving the aesthetic outcome of scars, combined with histopathological correlation of the response. PATIENTS AND METHODS: Thirty patients with scars of different etiologies undergone one session of nanofat injection and evaluation was done 6 months after the session. Efficacy of treatment was assessed clinically using Vancouver scar scale by two independent blinded dermatologists and histopathologically using image analysis system. RESULTS: The age of enrolled patients ranged from 18 to 40 years old. There was a statistically significant improvement on the total Vancouver scar scale regarding the height and pliability of the scars. Pathological evaluation showed an increase in epidermal thickness, increased number and density of collagen and elastic fibers along with neovascularization. CONCLUSION: Evidenced by clinical and pathological improvement, autologous nanofat injection is an effective strategy for treating scars of different etiologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".