Fluid mechanical modeling of the upper urinary tract
Bibliographic record
Abstract
The upper urinary tract (UUT) consists of kidneys and ureters, and is an integral part of the human urogenital system. Yet malfunctioning and complications of the UUT can happen at all stages of life, attributed to reasons such as congenital anomalies, urinary tract infections, urolithiasis and urothelial cancers, all of which require urological interventions and significantly compromise patients' quality of life. Therefore, many models have been developed to address the relevant scientific and clinical challenges of the UUT. Of all approaches, fluid mechanical modeling serves a pivotal role and various methods have been employed to develop physiologically meaningful models. In this article, we provide an overview on the historical evolution of fluid mechanical models of UUT that utilize theoretical, computational, and experimental approaches. Descriptions of the physiological functionality of each component are also given and the mechanical characterizations associated with the UUT are provided. As such, it is our aim to offer a brief summary of the current knowledge of the subject, and provide a comprehensive introduction for engineers, scientists, and clinicians who are interested in the field of fluid mechanical modeling of UUT. This article is categorized under: Cancer > Biomedical Engineering Infectious Diseases > Biomedical Engineering Reproductive System Diseases > Biomedical Engineering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".