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Record W3163224451 · doi:10.1002/jum.15750

Impact of Applying a Skin Compression With the Ultrasound Probe on Carotid Artery Strain Elastography

2021· article· en· W3163224451 on OpenAlexafffund
Boris Chayer, Marie‐Hélène Roy Cardinal, Vicky Biron, Noémie Cloutier, Clara Petit, Samuel Dubord, Louise Allard, Guy Cloutier

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMedicineElastographyUltrasoundCommon carotid arteryCompression (physics)Carotid arteriesLumen (anatomy)Biomedical engineeringInternal carotid arteryIntima-media thicknessRadiologyCardiologyInternal medicineMaterials science

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the impact of varying the external compression exerted by the ultrasound probe when performing a carotid strain elastography exam. METHODS: Nine healthy volunteers (mean age 43 years ±13 years; 6 men) underwent a vascular ultrasound elastography exam using a custom made sound feedback handle embedding the probe, and allowing the sonographer to adjust the applied compression. A clinical standard practice (SP) force was first recorded, and then predetermined compression (PDC) forces were applied, ranging from 0 to 5 N for the left common carotid artery (CCA) or 2-12 N for the left internal carotid artery (ICA). Six carotid elastography features, namely maximum and cumulated axial strains, maximum and cumulated shear strains, cumulated axial translation, and cumulated lateral translation were assessed with noninvasive vascular elastography (NIVE) on near and far walls of carotids. The carotid intima media thickness (IMT) and diameter were also measured. RESULTS: All elastography features on the near wall of both CCA and ICA decreased statistically significantly as the PDC force increased; this association was also observed for half of the features on the far wall. Three NIVE features at the lowest PDC force (out of 72 that were tested) were statistically significantly different than values at the SP force. Overall, NIVE showed some variance to probe compression with linear regression slopes revealing changes of 10.1%-45.6% in magnitude over the whole compression range on both walls. The maximum IMT for the ICA near wall, and carotid lumen diameters of both CCA and ICA were statistically significantly associated with PDC forces; these features underwent a decrease of 10.2%, 36.2%, and 17.6%, respectively, over the whole range of PDC force increase. Other IMT measurements were not statistically significantly associated with applied PDC forces. CONCLUSION: These results suggest the need of technical guidelines for carotid strain elastography. Using the lowest probe compression while allowing a good B-mode image quality is recommended to improve the robustness of NIVE 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 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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.303
Teacher spread0.290 · 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 designBench or experimental
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

Citations6
Published2021
Admission routes2
Has abstractyes

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