Ureteroscopic Doppler Ultrasonography: Mapping Renal Blood Flow from Within the Collecting System
Bibliographic record
Abstract
Introduction: Herein we provide the first report regarding in vivo porcine renal forniceal, papillary, and infundibular blood flow at the urothelial level using a novel ureteroscopic Doppler transducer. Materials and Methods: Nephroureteroscopy was performed on 11 female Yorkshire pigs to map the forniceal, papillary, and infundibular blood flow. A Doppler transducer was mounted to a 3F 120 cm catheter; the probe was passed through the working channel of a flexible ureteroscope. Blood flow was categorized from 0 (no flow) to 3 (highest flow) based on auditory intensity. At each site, a holmium laser probe was activated until it penetrated ∼1 cm into each of the examined areas; bleeding times were recorded. Results: The frequency of the Doppler transducer signal was proportional to the blood velocity within the vessel with expected increased bleeding times confirmed after puncture with a holmium laser. Analysis demonstrated that the 6 o'clock position of the fornix had significantly greater blood flow than any other forniceal location (p < 0.001). The center of each papilla had the least blood flow (p < 0.001). Blood flow was significantly higher at the infundibular level compared with the caliceal fornices at all locations (anterior, posterior, upper pole, midkidney, and lower pole) (p < 0.001). Conclusions: In a porcine model, a miniaturized Doppler ultrasound probe used during ureteroscopy demonstrated that the renal papilla had the least amount of blood flow whereas the infundibula had the highest blood flow. These data may serve to inform site selection during percutaneous nephrostomy placement.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".