Efficacy of autologous adipose-derived stem cells and fractional carbon dioxide laser in the treatment of atrophic linear scars
Bibliographic record
Abstract
Background Atrophic linear scars are scars that exhibit indentation or depression in the skin below the level of the surrounding tissues. Hyperpigmentation or hypopigmentation may be present. They are often caused by surgery, burns, and trauma. Objective To evaluate the efficacy of autologous adipose-derived stem cells (ADSCs) combined with fractional carbon dioxide (CO 2 ) laser in the treatment of atrophic linear scars. Patients and methods This randomized, controlled preliminary study included 20 patients aged 20–45 years of both sexes with facial atrophic linear scars. The patients were randomized systematically into two groups: group A (combined ADSCs and fractional CO 2 laser procedure) and group B (fractional CO 2 laser only) of 10 patients each. The scar was evaluated using the Vancouver scar scale, along with a subjective satisfaction questionnaire, and ultrasound skin analysis at baseline and 1 month after the third treatment session. Results All participants completed the study. The mean Vancouver scar scale score improved significantly from 3.60±0.69 to 1.20±0.79 and from 2.20±1.03 to 1.60±0.52 with fractional CO 2 laser plus ADSCs and CO 2 laser procedure, respectively ( P <0.001). However, CO 2 laser plus ADSCs was significantly superior. Ultrasound skin examination showed a statistically significant increase in dermal collagen density (thickness) in both groups. Conclusion Our study shows that treatment with CO 2 laser exposure plus ADSCs is safe and more effective for atrophic linear scar management than CO 2 laser exposure alone.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".