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Abstract: The Effectiveness of Early Combined Fractional CO2 and Pulsed Dye LASER Treatment after Scar Revision

2016· article· en· W2521296430 on OpenAlexaboutno aff
Seung Min Nam, Yong Bae Kim, Eun Soo Park, Young Ho Kim, Jin Young Kim, Yu Gil Park

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2016
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsHyperpigmentationHypopigmentationSurgeryAblative caseAdverse effectLaserDermatologyInternal medicineRadiation therapyOptics

Abstract

fetched live from OpenAlex

INTRODUCTION: Scars are one of the important complications of the wound healing process that cause physical, psychological and cosmetic effect on the patients. Scar revision and laser treatment have been used over the past century for the improvement of many different types of scars. The purpose of this study is to evaluate the effectiveness of early combined carbon dioxide ablative fractional laser (AFL) and the pulsed dye laser (PDL) after scar revision. MATERIALS AND METHODS: 14 patients underwent scar revision were enrolled for this study. All patients were treated with combined a 10,600 nm AFL and 595 nm PDL. The laser treatments were performed 2 weeks after scar revision surgery, which is the early remodeling phase, at 4 weeks intervals. Four treatments were performed. Vancouver Scar Scale (VSS) scores were evaluated before treatment and 5 months after final laser treatment. RESULTS: Each VSS scores presented statistically significant improvements except height (Fig. 1). There were no adverse complications such as wound disruption, hyperpigmentation and hypopigmentation during the follow up periods. CONCLUSION: This study shows that early combined ablative fractional CO2 and pulse dye laser treatments after scar revision are an effective and safe method to minimize scar formation. DISCLOSURE/FINANCIAL SUPPORT:None of the authors has a financial interest in any of the products, devices, or drugs mentioned in this manuscript.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.304
Teacher spread0.280 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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