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Record W4394892696 · doi:10.1093/jbcr/irae036.087

88 Lessons Learned Evaluating Ablative Fractional CO2 Laser for Burn-Related Donor Site Scars

2024· article· en· W4394892696 on OpenAlexaboutno aff
Cameron S D’Orio, Bonnie C Carney, Angela Golding, Melissa M McLawhorn, Rebekah R Allely, Jeffrey W. Shupp, Shawn Tejiram, Taryn E Travis

Bibliographic record

VenueJournal of Burn Care & Research · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAblative caseScarsSurgeryDermatology

Abstract

fetched live from OpenAlex

Abstract Introduction Hypertrophic scar (HTS) remains a comorbidity of burn injury, often requiring split thickness skin grafting (STSG) and resulting in symptomatic HTS at grafted sites and STSG donor sites (DS). Literature supports the use of ablative fractional CO2 laser (FLSR) to treat HTS, however many trials lack control sites and tissue-level examinations. Given the widespread adoption of FLSR for HTS, delegation of non-treated scar sites for the sake of RCT is troubling for many clinicians. We trialed using STSG DS scars for randomization rather than withholding FLSR from HTS at grafted sites. Methods Patients (n=25) were treated for DS scar with FLSR. DS scars were randomized and treated with either 5 FLSR treatments, follow-ups, and standard of care (SOC) or SOC only. Prior to treatment, DS skin and normal skin (NS) were evaluated for trans-epidermal water loss (TEWL), melanin index (MI), elasticity, and erythema. Serial biopsies were analyzed for epidermal thickness, rete ridge ratio (RRR), and papillary dermal cellularity. All sites, including a separate STSG scar site, were evaluated using the patient and observer scar assessment scale (POSAS) and Vancouver Scar Scale (VSS). Results Prior to treatment, DS skin had increased TEWL (10.5±0.8 vs 8.3±0.5 g/m2h, n=18; p=0.03), decreased RRR (1.1±0.0 vs 1.3±0.1, n=16; p=0.0001), and increased cellularity (8.8±0.9% vs 4.9 ± 0.6%, n=17; p=0.0014) compared to NS. DS skin and NS were not different in MI (p=0.07), erythema (p=0.77), elasticity (p=0.06), or epidermal thickness (p=0.32). Over time, control site DS and laser-treated DS were not different in TEWL (p=0.92), elasticity (p=0.45), erythema (p=0.99), RRR (0.97), cellularity (0.99), MI, epidermal thickness, POSAS-O score, POSAS-P score, or VSS (p>0.99). Over time, burn scar had increased skin elasticity (172.0±15.5 vs 78.5±13.2 N/m, n=17; p=0.0065). Burn scar did change in TEWL (p=0.53), MI (p=0.24), erythema (p=0.99), POSAS-O score (p=0.78), POSAS-P score (p=0.13), or VSS (p>0.99) over time. Conclusions NS and DS skin possess inherent physiological differences, though not to the degree of STSG HTS vs. NS. FLSR may not alter the rate of maturation and remodeling of DS scar compared to current SOC. While improvement in scar assessment was observed in laser-treated STSG HTS, no specific control for these sites was analyzed. Due to differences in pathophysiology of HTS formation at STSG sites and DS, DS may not be an adequate substitute for STSG HTS when designing RCTs to evaluate the effect of FLSR. Applicability of Research to Practice Prior studies evaluating the use FLSR consist of low-powered clinical trials or case studies without control sites or tissue level examinations, prompting the design of a RCT in DS scars. However, this scar type may not be suitable for this study design. Future work should extend to extra-cellular matrix morphology and transcriptomics of donor site and burn scar healing to better understand the effects of laser treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.220
GPT teacher head0.535
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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