Role of Vascular Function in Predicting Arteriovenous Fistula Outcomes: An Observational Pilot Study
Bibliographic record
Abstract
BACKGROUND: Many arteriovenous fistula (AVF) fail prior to use due to lack of maturation or thrombosis. Determining vascular function prior to surgery may be helpful to predict subsequent AVF success. This is a feasibility study to describe the vascular function in a cohort of chronic kidney disease (CKD) patients who are awaiting AVF creation. METHODS: A prospective cohort of 28 CKD patients expected to progress to HD underwent arterial stiffness (pulse wave velocity, PWV) and endothelial function testing (flow mediated dilation FMD, and peripheral arterial tonometry, PAT) one week prior to AVF creation. AVF success was defined as maintaining patency and achieving maturation. Post operative fistula assessment at 8 weeks evaluated maturation (clinical assessment of adequate fistula flowand ultrasound diameter ≥ 0.5 cm). RESULTS: The median age 72 years (62 - 78), 75% males, eGFR 15 ml/min/1.73 m(2) (12 - 18). 20 (71%) patients had successful AVF surgery with a mature AVF at 8 weeks. Patients with AVF success had higher mean PAT values 1.87 ± 0.52 than those with failed AVF 1.41 ± 0.24 p = 0.03. CONCLUSIONS: Microvascular endothelial function as measured using PAT may be useful as a predictor of AVF maturation and function. This simple non invasive marker of vascular function may be a useful tool to predict AVF outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".