The flow field near a venous needle in hemodialysis: A computational study
Bibliographic record
Abstract
The vascular access used in hemodialysis can suffer from numerous complications, which may lead to failure of the access, patient morbidity, and significant costs. The flow field in the region of the venous needle may be a source of damaging hemodynamics and hence adverse effects on the fistula. In this study, the venous needle flow has been considered, using three-dimensional computational methods. Four scenarios where the venous needle flow could potentially influence dialysis treatment outcome were identified and examined: Variation of the needle placement angle (10°, 20°, 30°), variation of the blood flow rate settings (200, 300, 400 mL/min), variation of the needle depth (top, middle, bottom), and the inclusion of a back eye in the needle design. The presence of the needle has significant effect on the flow field, with different scenarios having varying influence. In general, wall shear stresses were elevated above normal physiological values, and increased presence of areas of low velocity and recirculation-indicating increased likelihood of intimal hyperplasia development-were found. Computational results showed that the presence of the venous needle in a hemodialysis fistula leads to abnormal and potentially damaging flow conditions and that optimization of needle parameters could aid in the reduction of vascular access complications. Results indicate shallow needle angles and lower blood flow rates may minimize vessel damage.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".