Application of Physical Examination in Assessing Arteriovenous Access: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: A functional arteriovenous access is crucial for hemodialysis patients. Regular monitoring of vascular access allows for early detection and treatment of complications, which helps the access last longer and improves the effectiveness of dialysis. Clinical guidelines emphasize physical examination as the main way to monitor vascular access. To highlight the importance of physical examination among hemodialysis clinicians, this review investigates its application in AV access assessment. METHODS: We searched the China National Knowledge Infrastructure (CNKI), Wanfang, PubMed, Web of Science, Cochrane Library, Embase, ProQuest, and UpToDate databases from their inception to November 25, 2022, for studies on the physical examination of arteriovenous access. RESULTS: Physical examination exhibits acceptable sensitivity and specificity for detecting AV fistula maturation and stenosis. Quantitative physical examination indicators demonstrate high accuracy in assessing AV fistula stenosis and thrombosis. While widely used to evaluate vascular access function, dialysis practitioners often lack sufficient training and knowledge in physical examination. Therefore, comprehensive training for dialysis professionals in this area is essential. CONCLUSIONS: Several factors hinder the widespread use of physical examination in clinical settings. To optimize AV access care, hemodialysis managers should prioritize the development of standardized physical examination protocols that include quantitative indicators and assessment tools. Furthermore, regular training and competency assessments for dialysis staff are vital. Strengthening practitioners' physical examination proficiency will lead to earlier identification of AV access issues, greater access durability, and improved patient well-being.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".