Independent Prediction Factors for Primary Patency Loss in Arteriovenous Grafts within Six Months
Bibliographic record
Abstract
PURPOSE: The objective of this study was to explore the association between loss of primary functional patency within 6 months of first use and demographic and clinical characteristics in patients with arteriovenous grafts (AVGs) receiving chronic hemodialysis. The knowledge and management of these characteristics will minimize the proportion of catheterdependent dialysis patients for whom AVGs are the best choice. METHODS: This was a retrospective study of all chronic hemodialysis patients with AVGs followed by the Southern Alberta Renal Program from January 2005 to June 2008. Demographic and clinical variables and initial intra-access blood flow (IABF) were compared between those with and without loss of primary functional patency. To determine the contribution of independent variables to the dependant variable of loss of primary functional patency, a multivariable analysis using logistic regression was performed. RESULTS: The incidence of primary failure was 30% (107/359). Multivariable analysis found that low initial IABF (<650 mL/ min, odds ratio [OR] 31, P < 0.001), presence of diabetes (OR 3.5, P = 0.001), older age (>65 years OR 3.2, P< 0.001), and presence of peripheral vascular disease (OR 2.5, P< 0.005) were independently associated with loss of primary patency. CONCLUSIONS: AVGs are sometimes a better choice for those patients in which the time to and probability of successful fistula maturation may be a concern. Close monitoring of AVGs in patients with the identified risk factors associated with loss of primary patency may improve the life expectancy of the access.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".