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Comparison of ultrasound echo‐tracking technology and pulse wave velocity for measuring carotid elasticity among hemodialysis patients

2012· article· en· W2136398464 on OpenAlexvenueno aff
Ze‐Xing Yu, Xiangzhu Wang, Ruijun Guo, Yi‐Lun Zhou

Bibliographic record

VenueHemodialysis International · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsPulse wave velocityMedicineHemodialysisArterial stiffnessElasticity (physics)UltrasoundUltrasonic sensorCardiologyInternal medicineCreatinineRadiologyBlood pressureMaterials science

Abstract

fetched live from OpenAlex

This study aims to investigate the correlation between carotid elasticity in hemodialysis patients as evaluated by ultrasound echo-tracking technology and aortic pulse wave velocity. A total of 103 patients with end-stage renal disease who underwent stable hemodialysis were enrolled. An ultrasonic echo-tracking method was used to evaluate the elastic modulus and the stiffness index (β), which were compared with pulse wave velocity (PWV). Blood glucose, blood lipids, and serum creatinine were also tested. These indices were analyzed to determine the independent factor for arterial elasticity. The carotid elastic modulus and β were in good correlation with PWV among hemodialysis patients (P = 0.000). Diabetes and age are independent risk factors for arterial elasticity among hemodialysis patients. Ultrasound echo-tracking technology is a sensitive and accurate method for evaluating arterial elasticity and is a good alternative to traditional PWV.

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)0.005
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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.033
GPT teacher head0.308
Teacher spread0.275 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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