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Accuracy of Doppler Ultrasonography in Measuring Radial Artery Wall Thickness in Hemodialysis Patients: Comparison with Histologic Examination

2004· article· en· W2164047474 on OpenAlexvenueno aff
Y.o. Kim, J.i. Kim, Young Mi Ku, Yun‐Seok Choi, Hee Song, Dong Chan Jin, S.y. Kim, Euy-Jin Choi, Y-K. Chang, B K Bang

Bibliographic record

VenueHemodialysis International · 2004
Typearticle
Languageen
FieldMedicine
TopicVascular Procedures and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisDoppler effectRadial arteryArteriovenous fistulaRadiologyUltrasonographyWristArteryFistulaSurgeryCardiologyNuclear medicine

Abstract

fetched live from OpenAlex

Increased radial artery wall thickness (RAWT) is considered to be associated with early failure of radiocephalic arteriovenous fistula (AVF) as well as coronary artery atherosclerosis in hemodialysis patients. Therefore, exact measurement of RAWT by noninvasive method before the operation is very important. Objective: This study was designed to evaluate accuracy of Doppler ultrasonography in measuring RAWT in hemodialysis patients. Methods: This study enrolled 21 hemodialysis patients undergoing radiocephalic AVF operation for the first time. We measured RAWT (intima‐media thickness) using high‐resolution Doppler ultrasonography at the wrist before the AVF operation. We obtained specimens of the radial artery during the AVF operation and then measured RAWT by histologic examination. Results: Mean age of the patients was 60 ± 13 years and the number of females was 7 (33.3%). Mean values of RAWT measured by Doppler ultrasonography and histologic examination were 485 ± 93 μm (300–700 μm) and 426 ± 106 μm (300–700 μm), respectively. The value of RAWT of Doppler sonographic measurement well correlated with that of histologic measurement (r = 0.800, p < 0.001). Conclusion: Our data suggest that Doppler ultrasonography is an effective tool in measuring RAWT in hemodialysis patients before AVF operation.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.017
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.259
Teacher spread0.237 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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