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Record W3083080626 · doi:10.1097/scs.0000000000006983

The Effectiveness of Early Combined CO2 Ablative Fractional Laser and 595-nm Pulsed Dye Laser Treatment After Scar Revision

2020· article· en· W3083080626 on OpenAlexaboutno aff
Woo Jin Song, Seung Min Nam, Eun Soo Park, Chang Yong Choi, Sang Won Lee

Bibliographic record

VenueJournal of Craniofacial Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsAblative caseCarbon dioxide laserHypopigmentationSurgeryLaser treatmentAdverse effectLaserDermatologyLaser surgeryOpticsInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT: Scars are significant complications of wound healing and associated with negative physical, psychological, and cosmetic effects. Scar revision and laser treatment have been used over the past century to improve many different types of scars. Here, we evaluated the effectiveness of early combined carbon dioxide ablative fractional laser (AFL) and pulsed dye laser (PDL) treatment after scar revision. Fourteen patients who underwent scar revision were enrolled. All patients were treated with both a 10,600-nm AFL and a 595-nm PDL commencing 2 weeks after scar revision and continuing at 4-week intervals for a total of 4 treatments. Vancouver Scar Scale scores were evaluated before treatment and 5 months after the final treatment. All Vancouver Scar Scale scores improved significantly except that of scar height. We encountered no adverse complications (wound disruption, or hyper- or hypopigmentation) during follow-up. Early combined carbon dioxide AFL and PDL treatment after scar revision effectively and safely minimized scar formation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.298
Teacher spread0.273 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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