The role of surgery for assisted maturation after endovascular and percutaneous arteriovenous fistula creation
Bibliographic record
Abstract
Even in the best of circumstances, a significant number of patients will require adjunctive endovascular and/or surgical revision prior to achieving functional patency after endovascular or percutaneous AVF creation, at least within the United States. This rate appears to be higher after percutaneous AVF than after endovascular AVF, although because published reports of the former are mostly derived from American experience and those of the latter derived from experience outside the United States, it is unclear whether these differences are due to the technique itself or cultural and/or anatomic differences in dialysis access practices and patient populations. If arterial inflow is poor, this should be corrected first. When flow is adequate (perhaps 900 cc/min) but no single vein is cannulatable, a dominant suitable vein can be superficialized or transposed. If no suitable vein is dominant (most accurately assessed by using an intraoperative flowmeter), the best vein can be used, with or without occlusion of the other veins or reimplantation into the brachial artery. Finally, if the original anastomosis remains the sole supply to the cannulated vein, the original fistula has achieved assisted primary maturation (and assisted primary patency continues), while if a new arteriovenous anastomosis has been constructed, the original fistula has failed. We point out that for this reason as well as to best utilize the upper arm for later access, endovascular and percutaneous AVFs should be constructed and maintained within an atmosphere where both surgeons and non-surgeons work together on the overall access 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".