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Record W4396890669 · doi:10.1148/radiol.233136

Precision and Test-Retest Repeatability of Stiffness Measurement with MR Elastography: A Multicenter Phantom Study

2024· article· en· W4396890669 on OpenAlexaff
Efe Ozkaya, Paul Kennedy, Jun Chen, Octavia Bane, Jonathan R. Dillman, Kartik Jhaveri, Michael A. Ohliger, Phillip J. Rossman, Jean A. Tkach, John T. Doucette, Sudhakar K. Venkatesh, Richard L. Ehman, Bachir Taouli

Bibliographic record

VenueRadiology · 2024
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsWomen's College HospitalUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsMedicineImaging phantomRepeatabilityElastographyMulticenter studyNuclear medicineUltrasound elastographyRadiologyUltrasoundPathologyStatisticsRandomized controlled trial

Abstract

fetched live from OpenAlex

MR elastography demonstrated excellent stiffness measurement precision and test-retest repeatability across five study centers, 10 scanners, three manufacturers, two field strengths, two MR elastography sequences, and three phantoms mimicking different degrees of liver stiffness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2024
Admission routes1
Has abstractyes

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