Comparative Study between Fat Injection and Platelet Rich Plasma in Post Burn Facial Scar:Clinical and Histological Assessment
Bibliographic record
Abstract
Background& aim: There is no widely approved treatment procedure for post-burn scars, even though many therapy approaches have been promoted. Many previous studies separately evaluated fat injection and platelet rich plasma in treatment of post-burn scar. This study aimed to compare efficacy of both fat injection and platelet rich plasma in improving outcome of post burn facial scar. Patients& Methods: In the current study a total of 60 patients with post-burn facial scar were enrolled. Those patients were randomly subdivided into either fat injection group (n= 30) or platelet rich plasma group (PRP) (n= 30). Baseline characteristics were recorded in addition to Vancouver score (VS) used to assess the scar and histological evaluation.Results: Most patients were female with no significant differences among both groups regarding different baseline data and scar characteristics. There were no significant differences among the groups regarding baseline vancouver score (VS) and epidermal thickness, but the fat injection group had significantly lower VS (3.67 ± 1.83 vs 3.87 ± 1.33; p < 0.001) and higher epidermal thickness (362.20 ± 92.73 vs. 255.40 ± 90.00 (um); p < 0.001) during follow-up. There were only two (6.7percent) cases in the PRP group & four (13.3%) cases in the fat injection group developed conservatively relieved edema.Conclusion: the autologous fat injection was effective and safe technique in improving the outcome of post-burn facial scar as evaluated by Vancouver score and epidermal thickness. It’s recommended to perform such study on large number of patients in multiple centers with longer duration of follow up to confirm such findings.
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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) | 0.000 | 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".