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WFUMB Guidelines and Recommendations for Clinical Use of Ultrasound Elastography: Part 1: Basic Principles and Terminology

2015· review· en· W2127119044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUltrasound in Medicine & Biology · 2015
Typereview
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsFoothills Medical CentreUniversity of Calgary
FundersEngineering and Physical Sciences Research CouncilGE HealthcareSiemensCancer Research UKPhilips
KeywordsTerminologyUltrasound elastographyMedical physicsElastographyUltrasoundComputer scienceMedicineRadiologyLinguisticsPhilosophy

Abstract

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Conventional diagnostic ultrasound images of the anatomy (as opposed to blood flow) reveal differences in the acoustic properties of soft tissues (mainly echogenicity but also, to some extent, attenuation), whereas ultrasound-based elasticity images are able to reveal the differences in the elastic properties of soft tissues (e.g., elasticity and viscosity). The benefit of elasticity imaging lies in the fact that many soft tissues can share similar ultrasonic echogenicities but may have different mechanical properties that can be used to clearly visualize normal anatomy and delineate pathologic lesions. Typically, all elasticity measurement and imaging methods introduce a mechanical excitation and monitor the resulting tissue response. Some of the most widely available commercial elasticity imaging methods are ‘quasi-static’ and use external tissue compression to generate images of the resulting tissue strain (or deformation). In addition, many manufacturers now provide shear wave imaging and measurement methods, which deliver stiffness images based upon the shear wave propagation speed. The goal of this review is to describe the fundamental physics and the associated terminology underlying these technologies. We have included a questions and answers section, an extensive appendix, and a glossary of terms in this manuscript. We have also endeavored to ensure that the terminology and descriptions, although not identical, are broadly compatible across the WFUMB and EFSUMB sets of guidelines on elastography (Bamber et al., 2013Bamber J.C. Cosgrove D. Dietrich C.F. Fromageau J. Bojunga J. Calliada F. Cantisani V. Correas J.-M. D'Onofrio M. Drakonaki E.E. Fink M. Friedrich-Rust M. Gilja O.H. Havre R.F. Jenssen C. Klauser A.S. Ohlinger R. Săftoiu A. Schaefer F. Sporea I. Piscaglia F. EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 1: Basic principles and technology.Ultraschall Med. 2013; 34: 169-184Crossref PubMed Scopus (890) Google Scholar, Cosgrove et al., 2013Cosgrove D. Piscaglia F. Bamber J. Bojunga J. Correas J.-M. Gilja O.H. Klauser A.S. Sporea I. Calliada F. Cantisani V. D’Onofrio M. Drakonaki E.E. Fink M. Friedrich-Rust M. Fromageau J. Havre R.F. Jenssen C. Ohlinger R. Săftoiu A. Schaefer F. Dietrich C.F. EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 2: Clinical applications.Ultraschall Med. 2013; 34: 238-253Crossref PubMed Scopus (761) Google Scholar).

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.

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)0.003
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.332
GPT teacher head0.473
Teacher spread0.141 · 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