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Efficacy and safety of 1064‐nm Q‐switched Nd:YAG laser with low fluence for keloids and hypertrophic scars

2010· article· en· W2067188617 on OpenAlexaboutno aff
SB Cho, Ju Hee Lee, SH Lee, SJ Lee, Dongsik Bang, Sang Ho Oh

Bibliographic record

VenueJournal of the European Academy of Dermatology and Venereology · 2010
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHypertrophic scarsScarsHypertrophic scarVascularityErythemaLaserDermatologyTreatment modalityLaser treatmentFluenceLesionSurgeryIntense pulsed lightOptics

Abstract

fetched live from OpenAlex

BACKGROUND: Several treatment modalities using laser devices have been used for the treatment of keloids and hypertrophic scars with various therapeutic outcomes. OBJECTIVE: The purpose of this study was to describe the efficacy and safety of 1064-nm Q-switched (QS) Nd:YAG laser with low fluence on keloids and hypertrophic scars. METHODS: Keloids and hypertrophic scars located at 21 anatomic sites in 12 Korean patients (10 men and 2 women; mean age 23.8 years, range 21-33) were treated using 1064-nm QS Nd:YAG laser with low fluence at 1-2 week intervals. Treatment settings were 1.8-2.2 J/cm(2), 7-mm spot size and 5-6 passes with appropriate overlapping. RESULTS: Follow-up data collected 3 months after the final treatment revealed decreases in the mean score for the following lesion characteristics: pigmentation from 1.8 to 1.2; vascularity from 1.4 to 1.0; pliability from 3.0 to 2.0 and height from 2.3 to 1.8. The modified Vancouver General Hospital Burn Scar Assessment score decreased from 8.6 to 5.9 (P < 0.0001). Observed side-effects were a mild prickling sensation during treatment, and mild post-treatment erythema, both of which resolved within few hours. CONCLUSION: Our results demonstrate that QS Nd:YAG laser with low fluence may be used for the treatment of keloids and 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.480

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 integrity0.0000.001
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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

Citations54
Published2010
Admission routes1
Has abstractyes

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