The Effectiveness of Early Treatment With Intense Pulsed Light Combined With Fractional Erbium Laser in Preventing Post-traumatic Hypertrophic Scar Formation
Bibliographic record
Abstract
BACKGROUND: Once scars form and begin to proliferate, treatment becomes challenging. Traditional methods of scar treatment often provide suboptimal results. Therefore, early intervention has become widely accepted, with a focus on prevention during the wound-healing phase rather than later treatment. Here, the authors evaluate the effectiveness of early treatment with intense pulsed light (IPL) combined with fractional erbium laser in preventing the formation of post-traumatic hypertrophic scars. METHODS: A total of 120 patients who underwent emergency cosmetic suture surgery for facial trauma between January 2019 and December 2021 were selected for the study. The control group received conventional antiscar therapy (pressure therapy or antiscar medication), while the observation group received IPL combined with fractional erbium laser in addition to the conventional treatment. The specific treatment doses were adjusted based on the patient's age, scar color, texture, and thickness. A treatment course consisted of 3 to 5 sessions, with 4-week intervals between treatments. Follow-up was conducted within 1 year after treatment to assess the improvement in scar appearance before and after therapy. RESULTS: After IPL combined with fractional erbium laser treatment, patients in the observation group showed significantly lower scores in color, thickness, vascular distribution, softness, and total scores on the Vancouver Scar Scale (VSS) compared with the control group. During the follow-up, 3 complications were observed: 2 cases of skin blisters and 1 case of pigmentation. No immediate skin lesions, depigmentation, infections, ulcers, or other adverse reactions were reported. CONCLUSIONS: For patients with early-stage superficial scars following trauma surgery, early treatment with IPL combined with fractional erbium laser not only leads to significant improvements in appearance and effectively prevents hypertrophic scar formation but also promotes rapid recovery with few complications. This approach has clinical value.
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 | 0.000 |
| 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) | 0.001 | 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".