Real Time Intrarenal Pressure Control during Flexible Ureterorrenscopy Using a Vascular PressureWire: Pilot Study
Bibliographic record
Abstract
(1) Introduction: To evaluate the feasibility of measuring the intrapelvic pressure (IPP) during flexible ureterorenoscopy (f-URS) with a PressureWire and to optimize safety by assessing IPP during surgery. (2) Methods: Patients undergoing f-URS for different treatments were recruited. A PressureWire (0.014”, St. Jude Medical, Little Canada, MN, USA) was placed into the renal cavities to measure IPP. Gravity irrigation at 40 cmH2O over the patient and a hand-assisted irrigation system were used. Pressures were monitored in real time and recorded for analysis. Fluid balance and postoperative urinary tract infection (UTI) were documented. (3) Results: Twenty patients undergoing f-URS were included with successful IPP monitoring. The median baseline IPP was 13.6 (6.8–47.6) cmH2O. After the placement of the UAS, the median IPP was 17 (8–44.6) cmH2O. With irrigation pressure set at 40 cmH2O without forced irrigation, the median IPP was 34 (19–81.6) cmH2O. Median IPP during laser lithotripsy, with and without the use of on-demand forced irrigation, was 61.2 (27.2–149.5) cmH2O. The maximum pressure peaks recorded during forced irrigation ranged from 54.4 to 236.6 cmH2O. After the surgery, 3 patients (15%) presented UTI; 2 of them had a positive preoperative urine culture, previously treated, and a positive fluid balance observed after the surgery. (4) Conclusion: Based on our experience, continuous monitoring of IPP with a wire is easy to reproduce, effective, and safe. In addition, it allows us to identify and avoid high IPPs, which may affect surgery-related complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".