A Numerical Study to Precise the Estimation of the “Good” Mound Height in Endoscopic Treatment of VUR
Bibliographic record
Abstract
Objective: To ascertain the technique and volume of injection increasing the success rate of endoscopic VUR treatment, we develop a novel method to numerically describe the relationship between intramural ureter anatomy, intravesical pressure, and the theoretical mound height needed for adequate treatment. Methods: The main purpose of this study is to construct a finite element simulation of intramural ureter and injected mound which aims to numerically define the relationship between indexes which have influence in VUR endoscopic treatment. Using linearization software and numerically simulation data, the relationship between effective indexes has been derived. Results: By linearization of the effective parameters of different finite element models, the relationship between effective parameters in filling phase is derived as: H (m) = ﹣0.003467 (m) + 0.7864D (m) + 0.000233. This equation depicts adequate injected mound height as a function of internal diameter and intramural length, H = f(L, D). Conclusion: Using numerical simulation, we introduced the novel formula to predict the height of injected mound in endoscopic VUR treatment. As a result of this study, in order to increasing the success rate of this treatment, the ratio of mound height to intramural ureter diameter should be approximately 78%.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".