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Record W2401096213

Treatment of Hypertrophic Scars and Keloids Using Intense Pulsed Light

2009· article· en· W2401096213 on OpenAlexaboutno aff
You Jin Han, Yun Jeong, Kyu Kwang Whang

Bibliographic record

VenueLinchuang pifuke zazhi · 2009
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHypertrophic scarsScarsIntense pulsed lightVascularityHypertrophic scarDermatologySurgeryPatient satisfaction
DOInot available

Abstract

fetched live from OpenAlex

Background: Hypertrophic scars and keloids are prevalent and emotionally debilitating dermatologic diseases. Various treatment modalities have been advocated to treat hypertrophic scars and keloids. Objective: This study prospectively assessed the safety and efficacy of using intense pulsed light (IPL) on scars that originate from surgery. Methods: A total 22 patients with surgically induced hypertrophic scars and keloids were treated with IPL. Treatment was administrated at 4-week intervals, with an average of 3.5 sessions (range=1∼10). The scars were evaluated for pigmentation, pliability, height, vascularity, pain and pruritus by using the modified Vancouver Scar Scale (MVSS). The subjective assessment of satisfaction was scored by the patients on a 25% increment of satisfaction scale. Evaluations were performed monthly during the follow-up period. Results: There was overall clinical improvement for the appearance of the scars. Although statistically significant improvement was not shown (p=0.47), the average MVSS showed a trend for favorable effects on the scars with the successive IPL treatments. The patients who had higher baseline MVSS (10) demonstrated statistically significant improvements with the successive IPL treatments (p

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 score0.487
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.041
GPT teacher head0.331
Teacher spread0.290 · 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
Published2009
Admission routes1
Has abstractyes

Explore more

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