Independent Prediction Factors for Primary Patency Loss in Arteriovenous Grafts within Six Months
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it