An overview of AVF maturation and endothelial dysfunction in an advanced renal failure
Bibliographic record
Abstract
Abstract Life expectancy in patient with established kidney failure is considerably shortened with worsening quality of life. Through the provision of renal replacement therapy, survival and quality of life of advanced renal disease patients can be markedly improved. Haemodialysis and peritoneal dialysis are the treatment modalities in patients with end-stage renal disease. The efficiency of haemodialysis treatment relies on the functional status of vascular access. Vascular access and its related problems represent the main factors that determine a rise in the rate of incidence of the disease among haemodialysis patients and, consequently, a rise in the healthcare expenses. Arteriovenous fistula is the most efficient method, as it has a low risk of infection and mortality, and can ensure long-term functional access. However, maturation of an arteriovenous fistula is a complex process and is not well understood; significant numbers of arteriovenous fistula fail to develop sufficiently prior to their use for haemodialysis due to either lack of vessel maturation or spontaneous thrombosis. There are multiple blood markers and human factors that contribute to the maturation of fistula. Endothelial function is one of the most important determinants of arteriovenous fistula maturation. Early fistula failure is usually due to thrombosis which can be triggered by haematoma, by low flow rates resulting from low blood pressure, or by a hypercoagulable state. Impairment of endothelial function is associated with decreased arterial remodelling and final venous lumen diameter. Arteriovenous fistula anastomoses need early proliferation of endothelial cells to restore the barrier, permeability, and biochemical monitoring roles of endothelial cells in managing vascular repair, local thrombosis, neointimal hyperplasia, and inflammation. The purpose of this review was to discuss the maturation of AVF and endothelial dysfunction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".