Factors affecting the patency of arteriovenous fistulas for hemodialysis: Single center experience
Bibliographic record
Abstract
INTRODUCTION: Arteriovenous fistulas (AVFs) are the preferred form vascular access for hemodialysis (HD), as they have a low rate of complications and durable function. The aim of our investigation was to analyze the factors that might influence the function of AVFs. METHODS: Data were taken from the computerized patient record system in the Clinic of Urology and Nephrology, Clinical Center, Kragujevac, Serbia, for a 2-year period. We analyzed patients who had requested re-creation of AVFs as a secondary procedure. During this period 112 patients, 73 (65%) men and 39 (35%) women, had AVF thromboses. All relevant clinical and laboratory parameters that could affect the function and survival of AVF were evaluated. FINDINGS: In univariate logistic regression analysis, statistically significant predictors influencing the duration of the fistula were magnesium (P < 0.001), triglycerides (P = 0.041), smoking (P = 0.001), antiplatelet therapy (P < 0.001), and type of HD (bicarbonate vs. hemodiafiltration) (P < 0.001). In the multiple logistic regression model, high concentrations of magnesium (B = 7.434; P < 0.001) and antiplatelet therapy (B - 1.042; P = 0.04) were significantly associated with the length of AVF function. DISCUSSION: After successful establishment of an AVF, there is a compelling need to maintain fistula patency. Factors that affect functioning of the AVFs are presently under intense scrutiny. According to our results, some clinical factors may determine long term fistula duration, such as concentration of the magnesium and antiplatelet therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".