Predictors of steal syndrome in hemodialysis patients
Bibliographic record
Abstract
Steal syndrome is a feared complication of dialysis vascular access in a population becoming older and frailer. The aim of this study was to determine the predictor factors of steal syndrome. All proximal arteriovenous fistulas (AVFs), patent at day 30, inserted between January 2008 and December 2009 were studied. Data on age, gender, diabetes mellitus (DM) status, presence of coronary or peripheral artery disease, date of initiation of renal replacement therapy, date of access construction, localization, type of anastomosis, previous interventions, and outcome for AVF and patients were analyzed. There were 324 AVFs placed into 309 individual patients. The mean age was 66.7 ± 15.3 years, and the majority (53.7%) of the patients was male. Mean follow-up of all 324 fistulas was 18.6 ± 8.5 months. During follow-up, steal syndrome occurred in 26 (8%) of the AVFs. Univariate analysis revealed correlations between steal syndrome and DM (P = 0.002), brachiomedian fistulas (P = 0.016), and side-to-side (STS) anastomosis (P = 0.003). However, in multivariate analysis, the presence of DM, STS anastomosis, and female gender were found to be the independent risk factors. The strongest predictive factor was DM (odds ratio: 6.7; 95% confidence interval: 2.5-17.9). Being diabetic is the factor most predictive of having steal syndrome. In diabetic women, with a proximal access, it seems preferable to construct fistulas with end-to-side anastomosis to minimize the risk.
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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) | 0.001 | 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".