Soluble vascular endothelial growth factor receptor‐1 as a novel marker of arteriovenous fistula stenosis in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Arteriovenous fistula (AVF) stenosis is one of the most important clinical problems in hemodialysis patients. The histopathological findings of neointimal hyperplasia and impaired angiogenesis have been well established in stenotic AVFs. Soluble vascular endothelial growth factor receptor-1 (sVEGFR-1) has been implicated in pathological angiogenesis. Thus, we aimed to investigate the association between sVEGFR-1 and AVF stenosis in hemodialysis patients. METHODS: This prospective cohort study included 70 patients with end-stage renal disease. Forty-five patients were included in the final analysis, and the median follow-up period was 36 months. Venous stenosis was detected by physical examination and documented by fistulography. Blood samples were analyzed a day before the fistula operation, and serum levels of sVEGFR-1 were measured. FINDINGS: ) had the shortest stenosis-free survival (20 months [9.35-30.65]). Multivariate Cox analysis showed that sVEGFR-1, serum creatinine, and parathyroid hormone levels were associated with AVF stenosis risk. Kaplan-Meier survival curves showed that patients with less than the median value of sVEGFR-1 (<6093.07 pg/mL) had longer cumulative stenosis-free survival than patients with sVEGFR-1 levels above the median value (P < 0.001). DISCUSSION: Increased levels of sVEGFR-1 and obesity were found to be associated with AVF stenosis 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".