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Record W2623359529 · doi:10.1088/1361-6560/aa781f

Quantification of carotid artery plaque stability with multiple region of interest based ultrasound strain indices and relationship with cognition

2017· article· en· W2623359529 on OpenAlexaboutno aff
Nirvedh H. Meshram, Tomy Varghese, Carol Mitchell, Daren C. Jackson, Stephanie M. Wilbrand, Bruce P. Hermann, Robert J. Dempsey

Bibliographic record

VenuePhysics in Medicine and Biology · 2017
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Neurological Disorders and StrokeNational Cancer Institute
KeywordsUltrasoundMedicineCognitionStrain (injury)Stroke (engine)Region of interestCardiologyCommon carotid arteryCarotid arteriesNeuropsychologyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Vulnerability and instability in carotid artery plaque has been assessed based on strain variations using noninvasive ultrasound imaging. We previously demonstrated that carotid plaques with higher strain indices in a region of interest (ROI) correlated to patients with lower cognition, probably due to cerebrovascular emboli arising from these unstable plaques. This work attempts to characterize the strain distribution throughout the entire plaque region instead of being restricted to a single localized ROI. Multiple ROIs are selected within the entire plaque region, based on thresholds determined by the maximum and average strains in the entire plaque, enabling generation of additional relevant strain indices. Ultrasound strain imaging of carotid plaques, was performed on 60 human patients using an 18L6 transducer coupled to a Siemens Acuson S2000 system to acquire radiofrequency data over several cardiac cycles. Patients also underwent a battery of neuropsychological tests under a protocol based on National Institute of Neurological Disorders and Stroke and Canadian Stroke Network guidelines. Correlation of strain indices with composite cognitive index of executive function revealed a negative association relating high strain to poor cognition. Patients grouped into high and low cognition groups were then classified using these additional strain indices. One of our newer indices, namely the average L - 1 norm with plaque (AL1NWP) presented with significantly improved correlation with executive function when compared to our previously reported maximum accumulated strain indices. An optimal combination of three of the new indices generated classifiers of patient cognition with an area under the curve (AUC) of 0.880, 0.921 and 0.905 for all (n = 60), symptomatic (n = 33) and asymptomatic patients (n = 27) whereas classifiers using maximum accumulated strain indices alone provided AUC values of 0.817, 0.815 and 0.813 respectively.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.265
GPT teacher head0.356
Teacher spread0.091 · 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

Citations34
Published2017
Admission routes1
Has abstractyes

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