Monitoring and maintenance of arteriovenous fistulae and graft function in haemodialysis patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Several options exist for detecting and preventing stenosis in polytetrafluoroethylene grafts and arteriovenous fistulae for haemodialysis. Although observational studies show a significant benefit of such strategies, data from randomized trials are limited. This review describes recently published information that has helped to advance this field during the past year. RECENT FINDINGS: A new method for the measurement of access blood flow is discussed. This technique does not require special apparatus, which may facilitate its use in settings where resources are limited. The utility and potential shortcomings of access blood flow monitoring in grafts and fistulae are discussed, focusing on three key controlled studies published during the past year. Although much additional research is needed, regular access blood flow monitoring may improve outcomes in fistulae. Although there is less evidence that access blood flow monitoring is beneficial in grafts, regular dynamic venous pressure monitoring seems reasonable, because it can detect stenosis at a low capital cost. Neither radiotherapy nor combination therapy with aspirin and clopidogrel are useful for the prevention of stenosis in grafts. SUMMARY: Large randomized trials of screening appear feasible for both types of permanent vascular access. Given the adverse patient outcomes associated with access failure, as well as the high costs attributable to the implementation of ineffective screening strategies, such trials should be a high priority for nephrology researchers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".