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Record W4384930186 · doi:10.3390/ebj4030027

Investigation of the “Surgical Cuts CO2 Laser Therapy Technique” to Treat Minor Burn Scar Contractures in Children

2023· article· en· W4384930186 on OpenAlexaff
Jennifer Zuccaro, Lisa Lazzarotto, Jamil Lati, Charis Kelly, Joel Fish

Bibliographic record

VenueEuropean Burn Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsHospital for Sick Children
FundersAmerican Society for Laser Medicine and Surgery
KeywordsMuscle contractureMedicineContractureSurgeryLaserCarbon dioxide laserLaser therapyLaser surgeryOptics

Abstract

fetched live from OpenAlex

Fractional carbon dioxide (CO2) laser therapy has been shown to improve scar contractures following burns. However, the benefits of using other CO2 laser techniques to treat burn scar contractures are relatively unknown. This pilot study investigated a CO2 laser technique in which a series of perpendicular “surgical cuts” were created along the contracture. The aim of this study was to evaluate the effectiveness of using the “surgical cuts CO2 laser technique” in pediatric patients. This study included 12 participants with minor hand burn scar contractures that received one CO2 laser treatment using the surgical cuts technique. Trained assessors measured contractures pre- and post-laser therapy by assessing range of motion (ROM), digit length, and/or hand-span. All contractures were secondary to contact burns with the mean participant age equal to 5.5 years (SD 3.9). For all participants, at least one of the measured characteristics (ROM, hand-span, and digit length) improved after treatment. This pilot study demonstrated the benefit of using the surgical cuts CO2 laser technique to treat minor burn scar contractures. Future investigations are needed to further evaluate its effectiveness in comparison to the fractional CO2 laser therapy technique.

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 score0.025
Threshold uncertainty score0.258

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 studies0.0000.000
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 head0.032
GPT teacher head0.303
Teacher spread0.271 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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