Vascular mapping techniques: advantages and disadvantages
Bibliographic record
Abstract
At present, an arteriovenous fistula is the best available access when compared with an arteriovenous graft or a tunneled hemodialysis catheter. Preoperative vascular mapping has been shown to result in an increased placement of arteriovenous fistulae. In general, 3 modalities (physical examination, ultrasound examination and angiographic evaluation) are available for vascular evaluation. Both arterial as well as venous examination can be conducted using physical examination. However, this technique is known to miss veins, especially in the obese, and result in exclusion of patients who do not show adequate veins on clinical inspection, but who have suitable veins (proven by the other modalities) for AVF construction. Ultrasound examination of the vessels is an objective assessment. It provides an excellent evaluation of both arteries and veins for creation of an arteriovenous fistula. The technique is limited by its inability to directly visualize the central veins. Although imaging of the veins by the administration of radiocontrast dye optimally visualizes peripheral as well as central veins, it exposes the patient to the risk of radiocontrast-induced nephropathy. This article presents advantages and disadvantages of the 3 mapping techniques and proposes a strategy to conduct vascular mapping in patients with chronic kidney disease.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".