Hemodialysis Patient Preference for Type of Vascular Access: Variation and Predictors across Countries in the DOPPS
Bibliographic record
Abstract
PURPOSE: Catheters are associated with worse clinical outcomes than fistulas and grafts in hemodialysis (HD) patients. One potential modifier of patient vascular access (VA) use is patient preference for a particular VA type. The purpose of this study is to identify predictors of patient VA preference that could be used to improve patient care. METHODS: This study uses a cross-sectional sample of data from the Dialysis Outcomes and Practice Patterns Study (DOPPS 3, 2005-09), that includes 3815 HD patients from 224 facilities in 12 countries. Using multivariable models we measured associations between patient demographic and clinical characteristics, previous catheter use and patient preference for a catheter. RESULTS: Patient preference for a catheter varied across countries, ranging from 1% of HD patients in Japan and 18% in the United States, to 42% to 44% in Belgium and Canada. Preference for a catheter was positively associated with age (adjusted odds ratio per 10 years=1.14; 95% CI=1.02-1.26), female sex (OR 1.49; 95% CI=1.15-1.93), and former (OR=2.61; 95% CI=1.66-4.12) or current catheter use (OR=60.3; 95% CI=36.5-99.8); catheter preference was inversely associated with time on dialysis (OR per three years=0.90; 95% CI=0.82-0.97). CONCLUSIONS: Considerable variation in patient VA preference was observed across countries, suggesting that patient VA preference may be influenced by sociocultural factors and thus could be modifiable. Catheter preference was greatest among current and former catheter users, suggesting that one way to influence patient VA preference may be to avoid catheter use whenever possible.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".