Association Between Pulse Wave Velocity and Arteriovenous Fistula Stenosis in Hemodialysis Patients
Bibliographic record
Abstract
INTRODUCTION: Arteriovenous (AV) fistulas are widely used in hemodialysis patients, but their most common complication is stenosis. Stiffness at the arterial site of the AV fistula is believed to contribute to stenosis. This study examines whether arterial stiffness can predict the development of AV fistula stenosis. METHODS: Arterial stiffness was assessed using brachial-ankle pulse wave velocity (PWV). Chart reviews and laboratory records were analyzed. The diagnosis and intervention for AV fistula stenosis were performed using fistulography and percutaneous transluminal angioplasty. FINDINGS: A total of 80 patients were enrolled in the study. Over the 7-year follow-up period, 40 patients developed AV fistula stenosis. Univariate logistic regression analysis revealed that AV fistula stenosis was significantly associated with diabetes (OR: 4.68, 95% CI: 1.19-18.34, p = 0.03), average monthly cholesterol level (OR: 1.02, 95% CI: 1.00-1.04, p = 0.03), average monthly triglyceride level (OR: 1.01, 95% CI: 1.00-1.01, p = 0.02), and brachial-ankle PWV (OR: 1.61, 95% CI: 1.32-1.97, p < 0.01). In multivariate logistic regression analysis, only brachial-ankle PWV remained significantly associated with AV fistula stenosis (OR: 1.72, 95% CI: 1.34-2.22, p < 0.01). Receiver-operating characteristic curve analysis identified 16.75 m/s as the optimal cutoff value of brachial-ankle PWV for predicting the development of AV fistula stenosis. Patients with PWV > 16.75 m/s had a significantly higher risk of developing AV fistula stenosis compared with those with a PWV ≤ 16.75 m/s (HR: 4.71, 95% CI: 2.22-9.97, p < 0.01). DISCUSSION: Assessment of brachial-ankle PWV can improve the prediction efficacy of AV fistula stenosis development in hemodialysis patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".