Treatment of Traumatic Facial Atrophic Scars Using a Combined Laser Protocol Including Variable-Pulse Picosecond Technology: A Case Report
Bibliographic record
Abstract
The management of traumatic facial atrophic scars presents significant aesthetic and functional challenges. Traditional surgical approaches, while having documented efficacy, carry inherent risks of invasive procedures and the potential for creating secondary scarring. Therefore, this study aims to evaluate the effectiveness of a combined laser therapy approach, specifically utilizing potassium titanyl phosphate (KTP)/Nd:YAG, variable-pulse picosecond fractional laser, and Er:YAG lasers, as a non-surgical alternative for treating these complex traumatic scars. A 42-year-old man presented with a 30-day-old atrophic facial scar resulting from a sharp-point trauma. Given the patient's desire to avoid surgical intervention, a multimodal laser protocol was initiated over four sequential sessions. The treatment targeted various aspects of the scar pathophysiology, including vascular remodeling (KTP), deep collagen stimulation (long-pulse Nd:YAG), fractional micro-injury (picosecond), and precise resurfacing (Er:YAG). A progressive and significant reduction in scar depth and thickness was achieved, as documented by a marked improvement in the Vancouver Scar Scale (VSS) score, which dropped from a baseline of 10 to a final score of 1. The procedure was characterized by minimal adverse effects and resulted in high patient satisfaction. Combined laser therapy, integrating KTP/Nd:YAG, variable-pulse picosecond fractional, and Er:YAG lasers, represents a safe and effective alternative to surgical intervention for traumatic facial atrophic scars. This multimodal approach provides a means to achieve significant aesthetic improvement with minimal associated downtime and adverse effects. Further studies with larger patient populations and extended follow-up periods are warranted to confirm long-term efficacy and standardize treatment protocols.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".