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Record W4391822176 · doi:10.1177/12034754241227629

Laser Applications in Wound and Scar Management Post-Mohs Micrographic Surgery: A Systematic Review

2024· review· en· W4391822176 on OpenAlexaff
Michelle Le, Chaocheng Liu, Owen Dan Luo, Delaram Shojaei, Christopher D. Sibley

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsMcGill UniversityUniversity of British ColumbiaMcGill University Health Centre
Fundersnot available
KeywordsMedicineMohs surgerySurgeryWound healingKeloidMEDLINECochrane LibraryCINAHLDermatologyRandomized controlled trialPsychological intervention

Abstract

fetched live from OpenAlex

Mohs micrographic surgery (MMS) can lead to complications such as scarring and delayed wound healing, particularly in sensitive areas such as the face, neck, and chest. This study aims to assess the evidence regarding the use of lasers post-MMS for wound healing and scar revision. A comprehensive systematic review of the literature was performed using databases including MEDLINE, PubMed, EMBASE, Web of Science, Cochrane Library, and CINAHL from inception until July 25, 2022. A total of 2147 unique studies were identified, from which 17 were included in the analysis. A total of 17 studies reported applications of lasers with favourable efficacy including wound healing (n = 1), resurfacing of full-thickness skin grafts and split-thickness skin grafts (n = 4), periscar telangiectasias (n = 1), functional scar contractures (n = 2), and scar texture (n = 9). Minimal adverse effects were reported with the use of lasers post-MMS. Overall, the use of lasers post-MMS is a safe and well-tolerated option for scar revision with high patient satisfaction and is less invasive than surgical interventions.

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)0.002
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.289
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.366
Teacher spread0.317 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes1
Has abstractyes

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