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Record W2921005262 · doi:10.1093/jbcr/irz013.374

480 Elastography-Based Quantification of Burn Scar Stiffness

2019· article· en· W2921005262 on OpenAlexaff
Jennifer Zuccaro, María del Rosario Pérez, Arun Mohanta, Joel Fish, Andréa S. Doria

Bibliographic record

VenueJournal of Burn Care & Research · 2019
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineElastographyStiffnessSurgeryRadiologyUltrasoundStructural engineering

Abstract

fetched live from OpenAlex

The purpose of this study is to test the feasibility of using acoustic radiation force impulse (ARFI) ultrasound elastography to quantify the stiffness of hypertrophic burn scars. ARFI imaging is a non-invasive ultrasound technique that has been used to determine tissue stiffness in many fibroproliferative disorders including cutaneous scleroderma. Given that the technology is both objective and operator-independent, it has the potential to overcome the limitations inherent in using subjective clinical tools for assessing scar stiffness. Thirteen patients from the outpatient Burn Clinic at our tertiary pediatric hospital participated in this study. Only patients with a hypertrophic burn scar that was clinically diagnosed by a burn care specialist were eligible to participate. For each participant, a section of scar as well as a section of contralateral matched normal skin were marked for ultrasound measurement. ARFI ultrasound elastography was then carried out by a trained ultrasound technician to obtain values for the elastic modulus (E) of scar and control sites. Scar thickness was also measured using conventional gray-scale ultrasound. The Wilcoxon signed-rank test was used to compare scar and control sites and the Spearman’s correlation test was used to examine the relationship between scar stiffness and thickness. The results of this study show that hypertrophic scars are significantly stiffer than normal skin. More specifically, scarred areas were found to be approximately four times stiffer than the control sites (scar Emean = 42.68 kPa compared to control Emean = 10.05 kPa). Lastly, there was no correlation between scar stiffness and thickness (rs = 0.026; p > 0.05). We have shown that ARFI ultrasound elastography can be used to discriminate between scar and normal skin and should be considered a potentially valuable tool in the armamentarium of objective scar measures. Future research should focus on establishing reference data and determining the technology’s ability to detect scar changes over time and their responsiveness to treatment in longitudinal studies. The ability to objectively measure scar stiffness will allow clinicians to monitor scar progression (or regression) over time and evaluate response to 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 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.388
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 teacher head, not a consensus.

Study designBench or experimental
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

Citations1
Published2019
Admission routes1
Has abstractyes

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