Autologous micro-fat Injection in the treatment of post-traumatic atrophic scars
Bibliographic record
Abstract
Backround - Autologous fat grafting has been introduced as the treatment of atrophic scars and contour deformity. It not only serves to improve contour and to fill areas of deficiencies caused by trauma, deep burns or surgery, but increasingly there has been a focus on its ability to regenerate and remodel surrounding tissuesAim - to evaluate the efficacy and safety of microfat injection in post-traumatic atrophic scars using two objective methods Vancouver scar scale and patient oserver as a scar assessment scale.Patients and Methods - Thirty eight patients with atrophic posttraumatic scars with mean age 23.39 presenting to Dermatology outpatient clinic, Alazhar university hospital (Assuit) to inject microfat after scar subcision as a filling agent for the avoidance of scar redepression.Results - VSS, O-POSAS, and P-POSAS; (from 5.16 ± 1.33 to 4.37 ± 1.24), (from 18.47 ± 2.58 to 16.16 ± 2.14), and (from 15.16 ± 3.02 to 13.47 ± 1.62) respectively P. > 0.001, > 0.001, and 0.009 with significant differences in the two scales pre and post procedure (0.79 ± 0.96), (2.31 ± 3.12), and (1.68 ± 3.40) respectively.Conclusion - Autologous microfat injection is a valuable tool for the treatment of atrophic posttraumatic scars and have better results with a fewer 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 | 0.000 | 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".