Continuous Intrarenal Pressure Monitoring during Endourological Procedures for Stone Disease: A Canary in the Coalmine for Optimizing Patient Safety
Bibliographic record
Abstract
Background: Ureteroscopy is a widely used minimally invasive procedure for treating kidney stones. While ureteroscopy is generally safe and effective, it carries risks of complications that may be associated with elevated intrarenal pressure (IRP) during the procedure. This paper discusses the importance of monitoring and managing IRP during endourological procedures to mitigate the risk of complications. Summary: We conducted a review on IRP during endourological procedures, combining systematic and narrative approaches, to examine complications, clinical implications, and IRP monitoring practices. Preclinical and clinical studies have demonstrated strong associations between elevated IRP during endourological procedures and complication risk. Further, cumulative IRP exposure, which considers pressure magnitude and duration, may be a stronger predictor of complication risk than mean or peak IRP values alone. Surveys indicate that while many urologists acknowledge the clinical importance of monitoring and managing IRP, there remains a lack of awareness of real-time IRP monitoring technologies that can alert surgeons to elevated pressures and prompt immediate procedural modifications to mitigate complication risks. Key Messages: Based on current evidence, IRP monitoring should be considered for patients at high risk for pressure-related complications during endourological procedures, which includes a significant proportion of the patient population due to the prevalence of risk factors such as older age, female sex, diabetes mellitus, and obesity. A coordinated effort across the urological community is recommended to generate additional high-quality data to further our understanding of the potential benefits of real-time monitoring technologies. .
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".