Autologous nanofat injection in treatment of scars: A clinico‐histopathological study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| 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 it