Two-Year Cumulative and Functional Patency after Creation of Endovascular Arteriovenous Hemodialysis Fistulae
Bibliographic record
Abstract
PURPOSE: To assess 2-year cumulative and functional patency of endovascular arteriovenous fistulae (endoAVF) created with the WavelinQ device. MATERIALS AND METHODS: Patients who had fistulae created at a single center from December 2019 to December 2020 were included in this retrospective study. Forty-three patients underwent endoAVF creation (22 females, 21 males). Data collected included patient demographics, location of fistula creation, interventions performed, and brachial artery flow before and after creation. Two-year cumulative and functional patency rates were assessed with Kaplan-Meier method, and variables that affected patency and maturation were examined using Cox proportional hazards model. RESULTS: Technical success was 95% (41/43), and in 4 patients, the fistula did not mature for dialysis use (9.7%). For the remaining 37 patients with endoAVF maturation, 25 had ulnar-ulnar fistulae, 10 had radial-radial fistulae, and 2 had interosseous artery-vein fistulae. Mean maturity time was 73 days, and brachial artery flow of >886 mL/min was predictive of maturation. Mean tunneled dialysis catheter removal time was 133 days. Number of interventions per patient-year was 0.38, where 8 were maturation procedures (5 vein elevations/transpositions and 3 coil embolizations) and 21 were maintenance angioplasties. Two-year cumulative/secondary and functional patency rates were 89.4% and 92.1%, respectively, with a mean follow-up of 665.7 days. Examined variables did not impact cumulative or functional patency. One adverse event was migration of coil to the heart, which was successfully retrieved at time of procedure. CONCLUSIONS: Two-year patency of 89.4% and functional patency of 92.1% were observed after endoAVF creation with WavelinQ device.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".