Advanced Fractional CO₂ Laser Treatment for Steroid-Induced Atrophy Scars: Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND Steroid-induced atrophy is a common complication of intralesional corticosteroid use, characterized by thinning of the dermis and epidermis, reduced fibroblast activity, and subcutaneous fat atrophy. Traditional treatment modalities, including topical therapies, dermal fillers, and fat grafting, often yield variable results and present limitations such as invasiveness, temporary effects, or complications. This case report presents a novel therapeutic approach for the treatment of steroid-induced atrophic scars (SIAS) on the face, using fractional CO₂ laser therapy, recognized for its ability to promote dermal remodeling and scar improvement. CASE REPORT A 32-year-old woman presented with a single atrophic scar on her cheek, resulting from an intralesional corticosteroid injection. The patient underwent a preparatory phase involving mono polydioxanone (PDO) threads and dimethylaminoethanol (DMEA) skin booster to enhance dermal remodeling. She then received 2 sessions of fractional 10 600 nm laser therapy spaced 4 months apart, with tailored parameters: initial settings of 15 mJ/dot and 100 dots/cm², adjusted to 175 dots/cm² with Spread Half Fill Mode in the second session. This approach minimized downtime, with erythema resolving in 1-2 days, and avoided post-inflammatory hyperpigmentation (PIH). Clinical evaluations, including the Vancouver Scar Scale (VSS), showed significant improvement. The patient reported high satisfaction and improved quality of life. CONCLUSIONS Fractional CO₂ laser therapy represents a promising, minimally invasive treatment option for SIAS. The dual-session protocol highlighted in this report underscores its safety and efficacy. Future research should include larger trials, comparative studies, and parameter optimization for diverse populations.
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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) | 0.002 | 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".