Clinical epidemiology of arteriovenous fistula in 2007
Bibliographic record
Abstract
The native arteriovenous fistula (AVF) is considered the best access for hemodialysis due to its longer survival and lower complication rates as compared with other forms of vascular access. However, broad practice variation exists in the use of AVF among different countries and even within the same country among different regions and centers. Several barriers to AVF placement have been identified in the last decade that might explain its suboptimal use among both prevalent and incident patients. The present review summarizes and discusses recent findings from epidemiological studies on practice patterns and risk factors for AVF failure. Special emphasis is devoted to drawbacks and payoffs consequent upon the choice of the AVF as access for dialysis. In fact the AVF requires major investments in the short run but far less assistance and rework thereafter. Primary AVF failure, due to early failure or lack of maturation, is currently considered a key area of investigation to improve vascular access outcomes. The main challenge for the nephrologist today is to minimize the risk of primary failure while attempting to provide most patients with a native AVF. Improving vascular access outcomes is clearly a complex and difficult task. Recent experience from the United States suggests that multidisciplinary management is the most appropriate approach to deal with all the multifaceted aspects of end-stage renal disease care and to increase the likelihood of success.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".