MétaCan
Menu
Back to cohort
Record W3026435997 · doi:10.1016/j.burns.2020.05.009

Objective quantification of burn scar stiffness using shear-wave elastography: Initial evidence of validity

2020· article· en· W3026435997 on OpenAlexaboutno aff
Helen DeJong, S. Abbott, Marilyn Zelesco, Katrina Spilsbury, Mel Ziman, Brendan F. Kennedy, Lisa Martin, Fiona Wood

Bibliographic record

VenueBurns · 2020
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
FundersSiemens Healthineers
KeywordsMedicineScarsElastographyStiffnessWave velocityUltrasoundSurgeryShear (geology)Nuclear medicineRadiologyStructural engineeringComposite material

Abstract

fetched live from OpenAlex

Shear-wave elastography (SWE) is an ultrasound based technology that can provide reliable measurements (velocity) of scar stiffness. The aim of this research was to evaluate the concurrent validity of using both the measured velocity and the calculated difference in velocity between scars and matched controls, in addition to evaluating potential patient factors that may influence the interpretation of the measurements. METHODS: A cross-sectional study of 32 participants, with 48 burn scars and 48 matched contralateral control sites were evaluated with SWE, the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scale (POSAS) tactile sub-scores. RESULTS: Spearman's rho demonstrated high correlations (r > 0.7) between the measured scar velocity and both the POSAS and VSS pliability sub-scores, whereas moderate correlations (r > 0.6) were found with the calculated difference in velocity. Regression analysis indicated that the association of increased velocity in scars, varied by length of time after burn injury and gender. Body location and Fitzpatrick skin type also demonstrated significant associations with velocity, whereas age did not. CONCLUSION: SWE shows potential as a novel tool to quantify burn scar stiffness, however patient factors need to be considered when interpreting results. Further research is recommended on a larger variety of scars to support the findings.

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)0.000
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 score0.419
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.216
GPT teacher head0.371
Teacher spread0.155 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBurnsSame topicBurn Injury Management and OutcomesFrench-language works237,207