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Record W2142538028 · doi:10.1155/2014/204532

Effects of the Combined PDL/Nd:YAG Laser on Surgical Scars: Vascularity and Collagen Changes Evaluated by<i>In Vivo</i>Confocal Microscopy

2014· article· en· W2142538028 on OpenAlexaboutno aff
Krisztina Vas, Magdolna Gaál, Erika Varga, Réka Kovács, Balázs Bende, Ádám Kocsis, Lajos Kemény

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsScarsVascularityMedicineLaserSurgeryIn vivoOptics

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the efficacy of the sequential combined 585 nm PDL and the 1064 nm neodymium:yttrium-aluminium-garnet laser (PDL/Nd:YAG) in the treatment of surgical scars and to evaluate the short-term effects by in vivo confocal microscopy (RCM) and the long-term effects by clinical assessment of the scars. Twenty-five patients were enrolled with 39 postoperative linear scars; each scar was divided into two fields. One half was treated with the combined PDL/Nd:YAG laser, whereas the other half remained untreated. Each scar was treated three times at monthly intervals. Scars were evaluated by an independent examiner, using the Vancouver Scar Scale. The combined PDL/Nd:YAG laser significantly improved the appearance of the scars. In order to study the short-term effects of combined laser treatment, six additional patients were enrolled with 7 postoperative linear scars. One half of scars was treated once with the combined PDL/Nd:YAG laser. One week after this laser treatment, both the treated and the nontreated parts of the scars were examined by dermoscopy and RCM. The dermoscopic pictures revealed improvements even in treated areas. In conclusion, the combined PDL/Nd:YAG laser was found to be effective in improving the quality and appearance of the surgical 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.385
Teacher spread0.357 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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