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Record W2296141627 · doi:10.14730/aaps.2016.22.1.40

The Effectiveness of Ablative Fractional Carbon Dioxide Laser with Autologous Platelet Rich Plasma Combined Resurfacing for Hypertrophic Scar of the Shoulder

2016· article· en· W2296141627 on OpenAlexaboutno aff
DaWoon Lee, Eun Soo Park, Min Sung Tak, Seung Min Nam

Bibliographic record

VenueArchives of Aesthetic Plastic Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
FundersSoonchunhyang University
KeywordsMedicineAblative casePlatelet-rich plasmaCarbon dioxide laserScarsSurgeryHypertrophic scarLaserHypertrophic scarsUrologyPlateletInternal medicineLaser surgery

Abstract

fetched live from OpenAlex

Laser treatment for scars has improved over the past three decades.Autologous platelet-rich plasma (PRP) derived from whole blood is immunologically inert and contains a proper ratio of growth factors and cytokines.Here we describe the case of a 29-yearold female patient with a hypertrophic scar on her right shoulder caused by an operation performed in 2012.The patient underwent 11 laser therapy sessions with a fractional carbon dioxide (CO2) ablative laser system (LineXel) and two PRP injections.Her scar was evaluated with the Vancouver Scar Scale (VSS), and the baseline and post-treatment scores were 11 and 3, respectively.After treatment, the dimensions and volume of the scar were diminished, and contour, texture, and pigmentation had also improved compared to baseline.The patient reported less pain, swelling, and pigmentation following PRP combination ablative laser therapy.This case provides further evidence of the potential benefits of PRP as an adjuvant to fractional laser in reducing hypertrophic scars.

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 score0.066
Threshold uncertainty score0.397

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 studies0.0000.001
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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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