Hybrid Fractional Laser and Autologous Lipofilling: A Synergistic Strategy for Functional and Aesthetic Scar Remodeling
Bibliographic record
Abstract
Background: Scar management remains a significant challenge in plastic and reconstructive surgery, particularly when addressing atrophic, retractile, or fibrotic scars. Autologous fat grafting and hybrid fractional laser therapy have independently shown efficacy in improving scar quality. This study aims to evaluate the synergistic effect of their combination on clinical and functional scar outcomes. Methods: A prospective, comparative study was conducted on patients with cutaneous scars of various etiologies. Participants were treated with either hybrid fractional laser therapy alone (CO2 and 1570 nm Erbium-glass wavelengths) or a combined protocol of laser plus autologous lipofilling. Clinical outcomes were assessed at baseline and at 30, 60, and 90 days post-treatment using the Vancouver Scar Scale (VSS), patient satisfaction scores, and Visual Analog Scale (VAS) for pain and discomfort. Results: Patients receiving the combined treatment demonstrated significantly greater improvement in scar pigmentation, elasticity, pliability, and thickness compared to those treated with laser alone. Subjective symptoms, including pain and itching, were also more effectively alleviated. The volumetric and regenerative properties of adipose tissue, particularly its content of adipose-derived stem cells (ADSCs) and stromal vascular fraction (SVF), likely contributed to the enhanced outcomes observed. Conclusions: The combination of hybrid fractional laser therapy and autologous lipofilling offers a superior therapeutic strategy for scar remodeling compared to laser monotherapy. This integrated regenerative approach addresses both structural and biological aspects of scar tissue, making it a valuable protocol for personalized and effective scar management. Further randomized trials with larger sample sizes and histological validation are warranted to confirm these preliminary findings and refine therapeutic protocols.
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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 | 0.001 | 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".