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Record W2987717944 · doi:10.1080/09546634.2019.1687821

Fractional CO<sub>2</sub> laser is as effective as pulsed dye laser for the treatment of hypertrophic scars

2019· article· en· W2987717944 on OpenAlexaboutno aff
Mohammad Radmanesh, Samira Mehramiri, Ramin Radmanesh

Bibliographic record

VenueJournal of Dermatological Treatment · 2019
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsHypertrophic scarsDye laserLaserHypertrophic scarSurgeryPulse (music)Optics

Abstract

fetched live from OpenAlex

Background Both pulsed dye laser (PDL) and fractional CO2 laser (FCO2L) are used commonly for the treatment of hypertrophic scars.Objective To compare the efficacy of PDL and FCO2L on hypertrophic scars.Patients and methods One part of each scar, or one of the two similar scars in 35 patients was treated with PDL and the other parts, or scars were treated with FCO2L. The parameters used for FCO2L were: power = 30 W, pulse energy = 50 mJ, density = 200 spots/cm2. The parameters used for 585 nm PDL were 9 J/cm2 with 5 mm spot size. The FCO2L side was treated for three passes to debulk the scar. The coagulated tissue was wiped out before the next pass. The PDL side was treated with two superimposed passes. The procedures were repeated every month for 4 months.Results After four sessions of laser therapy, both sides showed remarkable improvement but no meaningful difference was detected between two areas that were treated with PDL and FCO2Ls (p > .05). The mean Vancouver Scar Scale was 7.31 ± 1.93 in the beginning and 4.26 ± 1.48 for FCO2L and 4.33 ± 1.70 for PDL one months after the final session.Conclusions Both PDL and FCO2Ls were equally effective on 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.026
GPT teacher head0.336
Teacher spread0.309 · 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 designBench or experimental
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

Citations18
Published2019
Admission routes1
Has abstractyes

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