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Record W4399140664 · doi:10.1097/dss.0000000000004245

The W-Plasty Serial Excision Method for Treating Medium Congenital Melanocytic Nevi: A Retrospective Analytical Study

2024· article· en· W4399140664 on OpenAlexaboutno aff
Xian Yan Luo, Yong Hu, Wen Jia Yang, Xiu Zu Song, Jian Peng

Bibliographic record

VenueDermatologic Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLesionSurgeryScarsZ-plastyRetrospective cohort studySurgical excision

Abstract

fetched live from OpenAlex

BACKGROUND: Serial excision remains the most commonly used surgical procedure for treating congenital melanocytic nevus (CMN). It is critical to remove as much of the lesion as possible with each procedure to reduce the number of procedures and to shorten the treatment duration. OBJECTIVE: To investigate the clinical efficacy of W-plasty serial excision for the repair of postoperative CMN defects. METHODS: A retrospective analysis of patients with medium CMN was conducted from April 2018 to March 2022. Treatment options were divided into elliptical serial excision (10 cases) and W-plasty serial excision (10 cases). RESULTS: Follow-up occurred over 6 months. The number of elliptical excision procedures was 2 to 4 (mean 2.9). The scar-to-lesion length ratio was 1.5 to 2.0 (mean 1.7). The mean Vancouver Scar Scale (VSS) score was 5.40 ± 0.42. The number of W-plasty excision procedures was 2 to 3 (mean 2.2). The scar-to-lesion length ratio was 1.2 to 1.5 (mean 1.4). The mean VSS score was 2.70 ± 0.26. W-plasty excision was superior to elliptical excision regarding the number of procedures and the effect on postoperative scars. CONCLUSION: W-plasty serial excision can be considered a suitable option for the excision of medium CMN, leading to excellent results.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.328
Teacher spread0.295 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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