Usefulness of assisted procedures for arteriovenous fistula maturation without compromising access patency
Bibliographic record
Abstract
INTRODUCTION: To increase the rate of arteriovenous fistula (AVF) use, assisted procedures for immature AVF have been strenuously performed. However, this is controversial in that an AVF matured by these assisted procedures may require more frequent intervention to maintain its patency, and have decreased long-term patency. METHODS: Eighty four AVFs that were matured with assisted maturation procedures and 266 AVFs that matured spontaneously without intervention, created between November 2009 and March 2013 from the hemodialysis (HD) vascular access (VA) cohort, were compared retrospectively and we also investigated the factors that may influence AVF long-term patency. Median follow-up was 26.8 months (interquartile range, 6.6-45.0 months). FINDINGS: Access survival did not differ between AVFs matured by assisted procedures and spontaneously mature AVFs (P = 0.29). In multivariate Cox regression analysis of AVF survival, age (HR, 1.029; 95% CI, 1.004-1.056; P = 0.024), maturation without assisted procedures 4-6 weeks after AVF creation (HR, 0.233; 95% CI, 0.107-0.506; P < 0.001), and AVF thrombosis (HR, 26.511; 95% CI, 10.986-63.978; P < 0.001) were significantly associated with AVF survival. Performance of assisted procedures to induce AVF maturation did not influence AVF survival (HR, 0.437; 95% CI, 0.191-1.002; P = 0.05). DISCUSSION: Our results support that idea that assisted maturation procedures can ensure the success of immature AVF without compromising long-term patency. These procedures can be considered more positively for increasing AVF use for VA placement in HD patients.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".