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Record W3046346838 · doi:10.1097/dss.0000000000002650

Fractionally Ablative Er:YAG Laser Resurfacing for Thermal Burn Scars: a Split-Scar, Controlled, Prospective Cohort Study

2020· article· en· W3046346838 on OpenAlexaboutno aff
William H. Sipprell, Derek E. Bell, Sherrif F. Ibrahim

Bibliographic record

VenueDermatologic Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScarsAblative caseTolerabilitySurgeryProspective cohort studyVisual analogue scaleDermatologyAdverse effectInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Thermal burn scars can have catastrophic impact on the quality of life and personal image, and over time can lead to profound physical and psychological debilitation. There are no established treatments to significantly improve burn scars. OBJECTIVE: To demonstrate the safety, efficacy, and tolerability of fractionally ablative Er:YAG resurfacing of mature burn scars. METHODS: Sixteen subjects were enrolled and received 3 treatments of fractionally ablative Er:YAG resurfacing at monthly intervals. Twelve completed the study. Scars were scored with the Vancouver Scar Scale (VSS) by the patient and physician before and after treatment. Blinded photographic analysis (Visual Analog Scale [VAS]) and blinded histologic analysis of tissue before and after treatment was also performed. RESULTS: Significant Improvement in VSS scores were seen in all 12 patients, reported by patients and the evaluating physician alike. Photographic analysis demonstrated subjective improvement in all 12 patients. Histologically, there was significant improvement in collagen architecture and the number of vessels per high-power field. The treatments were tolerated well by patients, and 1 superficial skin infection occurred. CONCLUSION: Fractionally ablative Er:YAG laser resurfacing is a safe and effective modality in the treatment of thermal burn scars with subjective and objective improvement as seen from the patient and physician.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.049
GPT teacher head0.321
Teacher spread0.272 · 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.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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