Efficacy of Early Application of Ablative Fractional CO<sub>2</sub>Laser on Secondary Skin Contracture after Skin Graft
Bibliographic record
Abstract
Background Ablative fractional carbon dioxide laser is widely used for the treatment of various scars including burn injuries.We applied ablative fractional CO2 laser on the skin graft scar of faces.Methods Fourteen patients between 2010 and 2013 who underwent facial skin graft were included in this study.The ablative fractional CO2 laser was applied to 7 patients in the laser therapy group.It was initiated at 5th week after the skin graft.Clinical photographs were taken, and Patient Scar Assessment Score (PAS) was obtained during every visit from a patient at the outpatient clinic and 4 weeks after the last laser treatment.In the untreated control group, clinical photographs and PAS were taken at 5th and 21st weeks after the skin graft.Vancouver Scar Scale (VSS) and Observer Scar Assessment score (OAS) was rated by single independent plastic surgeon with the clinical photographs. ResultsIn the laser therapy group, VSS, PAS, and OAS improved after fractional laser treatment.In the untreated group, VSS was also improved by the natural process of scar maturation.However, the laser treated group showed significant improvement compared with the untreated group. ConclusionsThe ablative fractional CO2 laser can be a viable option for the treatment of skin graft scar.Further study with sufficient patients and long term follow-up is necessary for definite conclusions.
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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) | 0.001 | 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".