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Record W4403028389 · doi:10.1097/scs.0000000000010649

Immediate Injections of Botulinum Toxin A After Surgical Excision for Ear Keloids: A Retrospective Study

2024· article· en· W4403028389 on OpenAlexaboutno aff
Shu Hua Li, Xiu Juan Shan, Zhen Hua Wang, Shu Jie Tao

Bibliographic record

VenueJournal of Craniofacial Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKeloidSurgeryScarsSurgical excisionVisual analogue scaleAdverse effectBotulinum toxinConcomitantRetrospective cohort studyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Ear keloids are pathologic scar hyperplasia in the ear region. The most therapeutic approach was surgical shave excision with radiation therapy. However, radiation therapy is easily delivered to healthy surrounding tissues. In the last years, injections with botulinum toxin type A (BTX-A) have been proven to improve surgical scars effectively in clinical trials. This study aimed to evaluate the effect of immediate injections of BTX-A after surgical excision for ear keloids. METHODS: From January 2020 to January 2023, 33 consecutive patients with ear keloids were enrolled. All patients underwent scar excision and revision at the same time when they needed BTX-A. It was injected into surgical wound closure immediately after surgery. The results of this study were evaluated at follow-up from 7 to 18 months using the Vancouver Scar Scale (VSS) and the Visual Analogue Scale (VAS). RESULTS: From January 2020 to January 2023, 33 patients received concomitant therapy of immediate injections of BTX-A after surgery for ear keloids. The patients were evaluated at follow-ups lasting 7 to 18 months. Only one case recurred within the follow-up period, and no adverse effects were reported. CONCLUSION: This study demonstrates that significant cosmetic outcomes in ear keloid treatment were achieved after early postsurgical BTX-A injections. The patients reported high satisfaction and few complications.

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 score0.027
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.350
Teacher spread0.323 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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