Numerical Simulation of Peristaltic Urine Flow in a Stented Ureter
Bibliographic record
Abstract
The capacity of ureteral stents to enhance the conveyance of urine from kidney to bladder is the critical function for patients that require them.The flow path in and around the stent is not a trivial one, particularly if some elements of peristalsis are present in the ureter.This paper details a numerical flow simulation for an axially symmetric stented ureter segment.The flow of urine through a stented, elastically-modeled ureter was considered under varying pressure gradients, bore (lumen) obstructions, and peristaltic deflections (waves).Peristaltic waves are combined with the pressure gradient developed between the kidney and bladder to provide a more accurate representation of the complex flow mechanics found within the ureter.Although it is recognized that peristalsis ceases or diminishes greatly after prolonged presence of a stent, in the time frame that it is active, detrimental consequences like reflux may occur.Several relationships from varying control parameters are determined to predict the onset of reflux as flow conditions within the ureter change.It was determined that occurrence of reflux is more likely as the peristaltic deflection or the obstruction of the stent bore increases.The threat of reflux is low if the pressure gradient between the kidney and bladder remains large.These simulations provide insight into the fluid behaviour within a stented ureter that could lead to optimized stent designs and reduce the possibility of reflux, infection, and discomfort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".