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Record W2996223001 · doi:10.1097/dss.0000000000002293

High- Versus Low-Density Fractional Laser in the Treatment of Hypertrophic Postburn Scars: A Randomized Clinical Trial

2019· article· en· W2996223001 on OpenAlexaboutno aff
Shereen O. Tawfic, Safinaz Sayed, Ahmed Nada, Doha Manaa, Suzan Shalaby

Bibliographic record

VenueDermatologic Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsHypertrophic scarsHypertrophic scarSurgeryStainRandomized controlled trialConfidence intervalInternal medicinePathologyStaining

Abstract

fetched live from OpenAlex

BACKGROUND: Fractional CO2 laser has been shown effective in improving pigmentation, pruritus, and tightness of hypertrophic burn scars. However, there is no consensus on the optimal treatment parameters. OBJECTIVE: To compare effectiveness of different densities of fractional CO2 laser in the treatment of mature hypertrophic burn scars. MATERIALS AND METHODS: The study included 25 patients, each with 3 or more mature hypertrophic burn scars. Scars were randomly assigned to treatment with low-, medium-, and high-density fractional CO2 laser. Each scar received 3 sessions of laser at 1-month interval. The degree of improvement was assessed clinically using Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS) scores, and histologically through evaluation of collagen (Masson's Trichrome stain) before and 1 month after end of therapy. RESULTS: High-density parameters showed significant higher improvement in VSS and POSAS assessment scores (p-value < .001). Pliability and relief are the most improved parameters. Histopathological evaluation revealed a significant drop in the mean area percent of collagen in the 3 used parameters, with highest improvement with high-density laser treatment (p-value < .001). CONCLUSION: High-density fractional CO2 laser treatment provides more improvement in burn scars both clinically and histopathologically.

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)0.001
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.085
GPT teacher head0.370
Teacher spread0.285 · 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 designRandomized trial
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

Citations19
Published2019
Admission routes1
Has abstractyes

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