Fractional CO₂ Laser (SCAR3 Scanner) for a Hypertrophic Retracting Cleft Lip Scar: A Case Report
Bibliographic record
Abstract
BACKGROUND Scars, particularly those that are hypertrophic and retracting, are a major difficulty in dermatological and plastic surgery. Laser therapy offers a versatile approach to managing hypertrophic lip scars, addressing aspects of scar tissue, such as vascularity and collagen. CASE REPORT This case report describes a 43-year-old woman treated for a hypertrophic, retracting lip scar using fractional CO₂ laser (10 W, 1500 µs dwell time, D-pulse, 500 µm spacing, stack 1, double pass). Two sessions, 56 days apart, led to marked aesthetic and psychosocial improvement without complications. Post-treatment care included 7 days of antibiotic ointment, followed by sun protection and nightly silicone gel. Images were captured before and after the second treatment. During the procedure, the patient reported a perceived pain level of 3 on a scale of 1 to 5, indicating a moderate and tolerable level of discomfort. The Modified Vancouver Scar Scale (mVSS) chart indicated an overall improvement in scar characteristics, especially in pliability, vascularity, and pigmentation, with minimal changes in height; pain and pruritus levels remained unchanged from before the therapy. After treatments, the patient expressed extreme satisfaction with the results achieved. She reported being "extremely content and satisfied" with the improvement in her scar after laser treatment. No significant adverse effects were observed. The estimated reduced daily activity time was approximately 1 week after each session, with normal activities resumed shortly thereafter. CONCLUSIONS This case highlights the potential of CO₂ laser treatment in managing a complex hypertrophic and retracting scar, leading to notable esthetic improvement and a positive impact on the patient's emotional well-being.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".