The Effect of Autologous Fat Grafting on Scar Quality and Appearance
Bibliographic record
Abstract
Background and objective: Scars not only impact individuals' well-being but also strain public healthcare systems, Therefore, there is a necessity for simpler and more effective scar treatment methods. Thus, we evaluated the efficacy of autologous fat grafting on scar quality and appearance. Methods: This prospective study at the Burn and Plastic Surgery Hospital in Sulaymaniyah, Iraq, from July 2021 to November 2023, included 20 patients aged 8-48 years with prominent scars. Fat was harvested from the abdomen primarily, processed by decantation or centrifugation, and injected into scars using a 2 mm cannula. Outcomes were assessed using the Vancouver Scar Scale at multiple postoperative intervals. Data analysis was performed using SPSS software, with P < 0.05 considered statistically significant. Results: This study evaluates the impact of fat grafting on scar quality using the Vancouver Scar Scale. Significant improvements were observed in pigmentation (P = 0.012) and pliability (P = 0.002), though vascularity (P = 0.17) and height (P = 0.15) did not show significant changes. Additionally, fat grafting markedly reduced pruritus (P = 0.019) but did not significantly impact pain levels (P = 0.18). The study shows an 80% increase in patient satisfaction after fat injection. Conclusion: The study found that autologous fat grafting is important for scar remodeling. It improves scar appearance, skin characteristics, volume, and contour. It also helps with symptoms like itching and pain. Autologous fat grafting has comprehensive benefits and can be an effective treatment for scars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".