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Record W2903813668 · doi:10.1002/jum.14862

Reproducibility of 2 Liver 2‐Dimensional Shear Wave Elastographic Techniques in the Fasting and Postprandial States

2018· article· en· W2903813668 on OpenAlexaff
Paul Simkin, Anand Rattansingh, Kuan Liu, John M. Hudson, Mostafa Atri, Hyun‐Jung Jang, Tae Kyoung Kim, Korosh Khalili

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsOntario Institute for Cancer ResearchPublic Health OntarioUniversity of TorontoOntario Medical Association
Fundersnot available
KeywordsMedicineIntraclass correlationPostprandialConfidence intervalReproducibilityTransient elastographyPearson product-moment correlation coefficientNuclear medicineUltrasoundCorrelation coefficientElastographyStatisticsInternal medicineMathematicsCirrhosisRadiologyLiver fibrosis

Abstract

fetched live from OpenAlex

OBJECTIVES: The purpose of this study was to compare the reliability and agreement of 2 methods of 2-dimensional (2D) shear wave elastography (SWE) on liver stiffness in healthy volunteers. We also assessed effects of the prandial state and operator experience on measurements. METHODS: Two operators, 1 experienced and 1 novice, independently examined 20 healthy volunteers with 2D SWE on 2 ultrasound machines (Aixplorer [SuperSonic Imagine, Aix-en-Provence, France] and Aplio 500 [Canon Medical Systems Corporation, Otawara, Japan]). Volunteers were scanned 8 times by the operators using both machines in fasting and postprandial states. Agreement was evaluated by a Bland-Altman analysis, and the correlation was assessed by the Pearson correlation and intraclass correlation coefficients (ICCs). An analysis of variance was conducted to determine the contribution of the machine, prandial state, and operator experience to the variability. RESULTS: Agreement assessed by Bland-Altman plots showed no statistically significant difference in measured liver stiffness between the machines (mean difference, -0.8%; 95% confidence interval, -3.7%, 2.1%), with a critical difference of 1.36 kPa. The correlation was good to excellent for both the crude overall Pearson coefficient and the ICC, both measuring 0.88 (95% confidence interval, 0.82, 0.92). Subclass ICCs for the fasting state, postprandial state, novice operator, and experienced operator were 0.89, 0.88, 0.90, and 0.86, respectively. The 2-way mixed effect analysis of variance showed that the volunteers accounted for 86.3% of variation in median liver stiffness, with no statistically significant contribution from operator experience, the prandial state, or the machine (P = .108, .067, and .296, respectively). CONCLUSIONS: Our study showed that the 2D SWE techniques had a high degree of reliability and agreement in measurement of liver stiffness in a healthy population. Operator experience and the prandial state did not impart significant variability to stiffness measurements.

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.157
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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