MétaCan
Menu
Back to cohort
Record W2315766829 · doi:10.1177/8756479314531853

Comparison and Accuracy of Carotid Plaque Analysis Between Two- and Three-Dimensional Ultrasound Imaging

2014· article· en· W2315766829 on OpenAlexaff
Lysa Legault Kingstone, Wael Shabana, Rebecca E. Thornhill, Megan A. White, Joanna Lam, Geoffrey Currie

Bibliographic record

VenueJournal of diagnostic medical sonography · 2014
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineVulnerable plaqueRisk stratificationRadiologyUltrasoundCarotid arteriesPathologyCardiology

Abstract

fetched live from OpenAlex

Plaque characterization using traditional two-dimensional (2D) imaging and/or three-dimensional (3D) ultrasonographic (US) techniques is a new method for evaluating artery wall morphology and plaque risk stratification. The purpose of this study was to assess and compare 2D and 3D US, measuring the interobservation differences for specific plaque-imaging analyses. Phantoms that simulated various types of atherosclerotic plaque pathology were imaged and findings reported by three experienced sonographers. Interobservation agreement and subanalyses were created. For each type of plaque pathology, agreement was moderate; however, conformity increased with the application of 3D US versus 2D US alone. Agreement was best for the identification of fissures, ulcerations, and irregular plaque surface. Advanced sonographic techniques for carotid plaque imaging provide a reproducible method in the analysis and morphologic assessment of simulated carotid atheromatous lesions, with superior interobserver variability. Three-dimensional US improves visualization of some pathologies and may provide additional information in the evaluation and risk stratification of vulnerable carotid plaque.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.008
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.292
Teacher spread0.283 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of diagnostic medical sonographySame topicCerebrovascular and Carotid Artery DiseasesFrench-language works237,207