Patient selection, education, and cannulation of percutaneous arteriovenous fistulae: An ASDIN White Paper
Bibliographic record
Abstract
End-stage kidney disease patients who are candidates for surgical arteriovenous fistula creation commonly experience obstacles to a functional surgical arteriovenous fistula, including protracted wait time for creation, poor maturation, and surgical arteriovenous fistula dysfunction that can result in significant patient morbidity. The recent approval of two endovascular devices designed to create a percutaneous arteriovenous fistula enables arteriovenous fistula creation to be placed in the hands of interventionalists, thereby increasing the number of arteriovenous fistula providers, reducing wait times, and allowing the patient to avoid surgery. Moreover, current studies demonstrate that patients with percutaneous arteriovenous fistula experience improved time to arteriovenous fistula maturation. Yet, in order to realize the potential advantages of percutaneous arteriovenous fistula creation within our hemodialysis patient population, it is critical to select appropriate patients, ensure adequate patient and dialysis unit education, and provide sufficient instruction in percutaneous arteriovenous fistula cannulation and monitoring. In this White Paper by the American Society of Diagnostic and Interventional Nephrology, experts in interventional nephrology, surgery, and interventional radiology convened and provide recommendations on the aforementioned elements that are fundamental to a functional percutaneous arteriovenous fistula.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".