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Record W4380538020 · doi:10.34172/jlms.2023.13

The Efficacy of Intense Pulsed Light in the Treatment of Keloids and Hypertrophic Scars

2023· article· en· W4380538020 on OpenAlexaboutno aff
Le Thai Van Thanh, Tran So Quan, Le Vi Anh, Ta Quoc Hung, Nguyen Lam Vuong

Bibliographic record

VenueJournal of lasers in medical sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntense pulsed lightScarsErythemaHypertrophic scarsDermatologyHyperpigmentationHypertrophic scarKeloidSurgery

Abstract

fetched live from OpenAlex

Introduction: Different therapies have been applied to keloids and hypertrophic scars. Intense pulsed light (IPL) has recently been used but the evidence is limited. This study was to evaluate the effectiveness and safety of IPL as monotherapy for keloids and hypertrophic scars. Methods: This was a before-and-after interventional study on 16 patients with 50 scars who underwent IPL. Seven scars receive one IPL session, seven received two sessions, and 36 received three sessions. Outcomes were evaluated by the Vancouver Scar Scale (VSS), Patient and Observer Scar Assessment Scale (POSAS), scar ultrasound, colorimeter for pigmentation and erythema, and side effects. Results: After the treatment, most outcomes significantly improved except that the pigmentation of the scars did not change. Scar thickness significantly reduced by nearly 10% after the first IPL session, 15% after the second session, and>20% after the third session. All side effects were mild with crust (33.3-46%), blisters (8.3-40%), and hyperpigmentation around the scar (0-14%); the pain was moderate as assessed by the patients. Conclusions: IPL is a safe and effective treatment for keloids and hypertrophic scars. More studies are required to confirm our results.

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)0.002
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 score0.252
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.380
Teacher spread0.329 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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