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Record W2150938126 · doi:10.14730/aaps.2014.20.2.114

Efficacy of Early Application of Ablative Fractional CO<sub>2</sub>Laser on Secondary Skin Contracture after Skin Graft

2014· article· en· W2150938126 on OpenAlexaboutno aff
Hyungwoo Yoon, Yoon-kyu Chung, Jiye Kim

Bibliographic record

VenueArchives of Aesthetic Plastic Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAblative caseScarsSurgeryHypertrophic scarContractureLaserCarbon dioxide laserLaser surgeryRadiation therapy

Abstract

fetched live from OpenAlex

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. Results In 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. Conclusions The 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.252
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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