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Record W4405997319 · doi:10.56397/crms.2024.12.03

Innovative Approaches to Scar Revision: Clinical Applications of Combined Laser Therapy and Biomaterials

2024· article· en· W4405997319 on OpenAlexaboutno aff
Cong Xu

Bibliographic record

VenueCurrent Research in Medical Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsnot available
Fundersnot available
KeywordsLaser therapyMedicineBiomedical engineeringMedical physicsNanotechnologyLaserMaterials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

This study aims to evaluate the clinical efficacy of combined laser therapy and biomaterials in scar revision. Through a randomized controlled trial, patients were randomly assigned to the treatment group and the control group. The treatment group received CO2 laser therapy combined with collagen or hyaluronic acid biomaterials. The primary assessment indicators included the visual analog scale (VAS) for scar, the Vancouver Scar Scale (VSS) score, and patient satisfaction surveys. The study results showed significant improvements in the appearance, texture of the scar, and patient satisfaction in the treatment group. Long-term follow-up studies further confirmed the durability of the treatment effects. The conclusion of this study supports the combination of laser therapy and biomaterials as an effective method for scar revision. Future research should focus on personalized treatment strategies and the assessment of long-term effects.

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)0.009
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.002
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 head0.663
GPT teacher head0.572
Teacher spread0.090 · 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 designOther design
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

Citations0
Published2024
Admission routes1
Has abstractyes

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