The Efficacy and Safety of Ablative Fractional Resurfacing Using a 2,940-Nm Er:YAG Laser for Traumatic Scars in the Early Posttraumatic Period
Bibliographic record
Abstract
BACKGROUND: Skin injuries, such as lacerations due to trauma, are relatively common, and patients are very concerned about the resulting scars. Recently, the use of ablative and non-ablative lasers based on the fractional approach has been used to treat scars. In this study, the authors demonstrated the efficacy and safety of ablative fractional resurfacing (AFR) for traumatic scars using a 2,940-nm erbium: yttrium-aluminum-garnet (Er:YAG) laser for traumatic scars after primary repair during the early posttraumatic period. METHODS: Twelve patients with fifteen scars were enrolled. All had a history of facial laceration and primary repair by suturing on the day of trauma. Laser therapy was initiated at least 4 weeks after the primary repair. Each patient was treated four times at 1-month intervals with a fractional ablative 2,940-nm Er:YAG laser using the same parameters. Post-treatment evaluations were performed 1 month after the fourth treatment session. RESULTS: All 12 patients completed the study. After ablative fractional laser treatment, all treated portions of the scars showed improvements, as demonstrated by the Vancouver Scar Scale and the overall cosmetic scale as evaluated by 10 independent physicians, 10 independent non-physicians, and the patients themselves. CONCLUSIONS: This study shows that ablative fractional Er:YAG laser treatment of scars reduces scars fairly according to both objective results and patient satisfaction rates. The authors suggest that early scar treatment using AFR can be one adjuvant scar management method for improving the quality of life of patients with traumatic scars.
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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 | 0.001 |
| 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".