A Dedicated Vascular access Program can Improve Arteriovenous Fistula Rates without Increasing Catheters
Bibliographic record
Abstract
PURPOSE: We describe the development and implementation of a comprehensive multidisciplinary vascular access (VA) program and describe its impact on VA distribution rates. METHODS: A retrospective review of all incident and prevalent patients in our hemodialysis (HD) unit was conducted in September 2001 to determine baseline data including: type of VA along with patient characteristics and comorbidities. Similar data was extracted from the database in 2005 for incident and prevalent patients. RESULTS: The VA program had a significant impact on arteriovenous fistulae (AVF) rates in both incident and prevalent HD patients: incident AVF rates increased from 14 to 39% (p=0.04) and prevalent AVF rates from 60 to 64% (p=0.015). Multivariate analysis revealed that male gender (OR 1.79 [CI 0.85-0.98, p=0.006]) and year of dialysis initiation 2005 vs. 2001 (OR 1.65 [CI 1.09-2.5, p=0.017]) were associated with AVF use among prevalent HD patients. Furthermore, age (per 5 years over 70) is associated with a decreased likelihood of having an AVF (OR 0.91 [CI 0.85-0.98, p=0.009]) whereas comorbidities of cardiovascular disease and diabetes had no impact. CONCLUSION: We demonstrate that a structured VA program can increase the number of functioning fistulas without a corresponding increase in catheters in incident and prevalent HD patients.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".