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Record W4410999028 · doi:10.3390/ebj6020029

Cosmetic Outcomes of the First Bodybuilder Using a Low-Cost Modified Culture Technique for Burn Wound Coverage: A Case Report and Long-Term Follow-Up

2025· article· en· W4410999028 on OpenAlexaboutno aff
W G Kleintjes, Tarryn Kay Prinsloo

Bibliographic record

VenueEuropean Burn Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
FundersUniversiteit Stellenbosch
KeywordsMedicineSurgeryGroin

Abstract

fetched live from OpenAlex

Cultured epidermal autografts (CEAs) serve as an alternative permanent skin replacement, though high costs often limit their use in resource-constrained settings and to life-saving cases. This case report presents the first documented cosmetic application of a modified CEA technique in a bodybuilder, demonstrating favorable aesthetic outcomes. A 28-year-old Black male with a 20% total body surface area burn sustained in a domestic fire exhibited superficial and deep partial-thickness burns to the face, arms, torso, and feet. Refusing grafts from visible donor sites, treatment using a low-cost modified CEA approach was employed to minimize donor site morbidity. Keratinocytes harvested from a groin biopsy were cultured on Cutimed Sorbact® (Essity AB, BSN Medical (Pty) Ltd., Pinetown, RSA) dressings with autogenous plasma and hydrogel supplementation and incubated at 37 °C for two weeks. Xenografts provided temporary coverage before CEA transplantation. Graft take was 85%, with minor (15%) loss at 21 days, requiring small autograft coverage. At two months, the Vancouver Scar Scale score was 4, indicating optimal pigmentation, smoother texture, and minimal scarring. These findings align with limited studies on CEAs for cosmetic applications, suggesting this cost-effective technique may broaden the scope of CEAs beyond life-saving interventions to include aesthetic reconstruction, reducing both donor site morbidity and scarring.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.330
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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