Robotic-assisted laparoscopic pyeloplasty for ureteropelvic junction obstruction
Bibliographic record
Abstract
INTRODUCTION: At present, there is no literature on the outcomes of robotic-assisted laparoscopic pyeloplasty (RALPyelo) in a Canadian context. Our objective was to perform a retrospective review of RALPyelo cases at a high-volume Canadian center. METHODS: We performed a retrospective review of patients who underwent RALPyelo at St. Michael's Hospital, between January 2012 and May 2019. Demographics, operative details, and pre- and postoperative imaging results (ultrasounds, computed tomography [CT] scans, and diuretic renal scan [DRS ]) were recorded. Patients were excluded if at least one-year followup data was unavailable. Our primary outcome was clinical and radiologic improvement defined as 1) symptom improvement; 2) stable/improved split renal function on DRS ; and 3) either improvement in the degree of hydronephrosis on ultrasound or CT, or improved drainage time on DRS. Secondary outcomes included postoperative complications, need for diagnostic intervention, and reintervention for recurrent UPJO. RESULTS: A total of 156 patients underwent RALPyelo after exclusions. The median age was 42 and 66% were female. Mean followup was 2.5 years. For our primary outcome, 87% had clinical and radiologic improvement. Diagnostic investigation for possible recurrent/persistent obstruction, based on symptoms and/or imaging results, was required in 17% of cases, but only 3% required reintervention for recurrent UPJO. Accordingly, the overall treatment success was 97%. The most common postoperative complication was urinary tract infection (18%), and urine leak was seen in only 2% of patients. CONCLUSIONS: The results of our study compare favorably with currently reported outcomes in the literature and demonstrate the safety and high level of success of RALPyelo at a high-volume Canadian center.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".