Radiocephalic Arteriovenous Fistula Patency and Use
Bibliographic record
Abstract
We sought to confirm and extend the understanding of clinical outcomes following creation of a common distal autogenous access, the radiocephalic arteriovenous fistula (RCAVF). Background: Interdisciplinary guidelines recommend distal autogenous arteriovenous fistulae as the preferred hemodialysis (HD) access, yet uncertainty about durability and function present barriers to adoption. Methods: Pooled data from the 2014-2019 multicenter randomized-controlled PATENCY-1 and PATENCY-2 trials were analyzed. New RC-AVFs were created in 914 patients, and outcomes were tracked prospectively for 3-years. Cox proportional hazards and Fine-Gray regression models were constructed to explore patient, anatomic, and procedural associations with access patency and use. Results: Mean (SD) age was 57 (13) years; 45% were on dialysis at baseline. Kaplan-Meier estimates of 3-year primary, primary-assisted, and secondary patency were 27.6%, 56.4%, and 66.6%, respectively. Cause-specific 1-year cumulative incidence estimates of unassisted and overall RC-AVF use were 46.8% and 66.9%, respectively. Patients with larger baseline cephalic vein diameters had improved primary (per mm, hazard ratio [HR] 0.89, 95% confidence intervals 0.81-0.99), primary-assisted (HR 0.75, 0.64-0.87), and secondary (HR 0.67, 0.57-0.80) patency; and higher rates of unassisted (subdistribution hazard ratio 1.21, 95% confidence intervals 1.02-1.44) and overall RCAVF use (subdistribution hazard ratio 1.26, 1.11-1.45). Similarly, patients not requiring HD at the time of RCAVF creation had better primary, primary-assisted, and secondary patency. Successful RCAVF use occurred at increased rates when accesses were created using regional anesthesia and at higher volume centers. Conclusions: These insights can inform patient counseling and guide shared decision-making regarding HD access options when developing an individualized end-stage kidney disease life-plan.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".