Risk Factors Associated with Patency Loss of Hemodialysis Vascular Access within 6 Months
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Clinical guidelines support vascular access surveillance to detect access dysfunction and alter the clinical course by radiologic or surgical intervention. 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 of patients receiving chronic renal replacement therapy with arteriovenous fistulas. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This was a retrospective study of all chronic hemodialysis patients followed by the Southern Alberta Renal Program from January 1, 2005 to June 30, 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 10% (81 of 831). Multivariable analysis found that older age (>65 years, odds ratio [OR] 3.6, P < 0.001), history of diabetes (OR 2.3, P = 0.007), history of smoking (OR 4.3, P < 0.001), presence of forearm fistulas (OR 4.0, P < 0.001), and low initial IABF (<500 ml/min, OR 29, P < 0.001) were independently associated with loss of primary patency. CONCLUSIONS: The set of patient risk factors identified in this study, particularly initial IABF, can be used to identify patients who are most at risk for developing vascular access failure and to guide a more directed approach for a vascular access screening protocol.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".