Instituting robotic pediatric urologic surgery in the Canadian healthcare system: Evaluating the feasibility and outcomes of robot-assisted pyeloplasty and ureteric reimplantation
Bibliographic record
Abstract
INTRODUCTION: Robotic pediatric urologic surgery has gained wide-spread adoption over the last decade. This article describes our experience in instituting the first pediatric urologic robotic surgery program in Canada. We evaluated the feasibility and safety of instituting pediatric robot-assisted urologic surgery and report our early outcomes for robot-assisted pyeloplasty (RAP) and ureteric reimplantation (RUR). METHODS: We prospectively evaluated all patients undergoing RAP and RUR by a single surgeon from June 2013 to March 2019. Demographic and clinical data were prospectively collected and included sex, age, and preoperative grade of hydronephrosis or reflux. Descriptive statistics were performed, and comparisons were made using Student's t-tests where appropriate. Success was defined as resolution or significant improvement of hydronephrosis following RAP and absence of recurrent urinary tract infection (UTI) and/or persistent vesicoureteric reflux (VUR) following RUR. Complications were described using the Clavien-Dindo system. RESULTS: A total of 52 RAPs and 24 RURs were performed with a minimum of six months followup. Forty-five RAP patients met criteria for success, while diagnostic imaging of success in the form of MAG-3 Lasix renograms was documented in the remaining seven for an overall success of 100%. Sixteen RUR patients met criteria for success and seven showed resolution of VUR on imaging following their first UTI, for an overall success rate of 96%. Operative times progressively improved from 204±35 minutes to 121±15 minutes in the RAP group and from 224±52 to 132±39 minutes in the RUR group. In the RAP cohort, one Clavien grade II and four Clavien grade III complications were noted, while three Clavien grade III complications were noted in the RUR cohort. CONCLUSIONS: Despite limited case volumes, robotic pediatric urologic surgery can be integrated into the Canadian healthcare system with success rates comparable to reported literature. However, compared to open surgery, RAP, and especially RUR, warrant further study to ensure lack of significant complications noted in our study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".