Early cannulation arteriovenous graft (Acuseal) versus standard arteriovenous graft in patients with end‐stage renal disease
Bibliographic record
Abstract
INTRODUCTION: The use of Acuseal arteriovenous graft (AAVG) is spreading in end-stage renal disease (ESRD) patients for its advantages in early cannulation. However, comparison of clinical outcomes between AAVGs and standard arteriovenous grafts (SAVGs) is limited. In this study, we compared the performance of AAVGs and SAVGs. METHODS: Consecutive ESRD patients underwent prosthetic vascular access between October 2017 and May 2019 at a single center were identified. Patients were divided into AAVG group and SAVG group. Patients' demographics, perioperative characteristics, and clinical outcomes were collected. Primary, primary assisted, and secondary patency rates were compared using Kaplan-Meier analyses. Postsurgery complications were compared using chi-square test or the Fisher's exact test. FINDINGS: A total of 304 arteriovenous grafts (AVGs) were implanted in 145 males and 159 females (mean age, 60.1 years; range, 20-91 years), comprising 143 AAVGs and 161 SAVGs. Median time to first cannulation was 3 days (interquartile range [IQR], 1-15 days) in the AAVG group and 30 days (IQR, 20-52 days) in the SAVG group (P < 0.001). Adjusted primary, primary assisted, and secondary patency at 12 months were not significant different between AAVGs and SAVGs (P = 0.911, P = 0.945, and P = 0.640, respectively). There was no statistical significance in regards to thrombosis (AAVG, 34.3%; SAVG, 26.1%; P = 0.120) and infection (AAVG, 4.9%; SAVG, 2.5%; P = 0.261) between the groups. DISCUSSION: Acuseal grafts provide comparable patency, and complication rates to SAVGs, with less time to first cannulation from the graft implanted.
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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 | 0.002 |
| 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".