Quality assessment of robotic repair of benign ureteral strictures
Bibliographic record
Abstract
INTRODUCTION: Endourologic treatments are first-line interventions for short ureteral strictures. With long strictures and endourologic failures, open repair has historically been used; however, robotic-assisted approaches have recently been shown to be effective. As a quality measure, we wanted to assess the performance of robotic ureteral reconstruction compared with open surgical repair during our transition to robotic surgery at a Canadian tertiary care center. METHODS: From 2011-2024, 43 complex ureteral stricture cases (19 open, 24 robotic) were performed. The primary outcome was six-month success defined by a composite of stent/pain-free status and renogram elimination half-life (T½). Secondary outcomes included length of stay, operative time, estimated blood loss, and complications. RESULTS: Success rates at six months were non-significantly different between robotic and open repair (83% vs. 79%, p=0.36). Length of stay was shorter in the robotic group (3.1±1.9 vs. 4.9±3.3 days, p=0.018). Estimated blood loss (231±84 vs. 244±170 mL, p=0.30) and operative time (220±67 vs. 214±74 minutes, p=0.40) were comparable between groups. Complication rates were similar between groups. CONCLUSIONS: Overall, robotic reconstruction yields equivalent six-month success to open repair, with shorter length of stay. These findings support continuing robotic-assisted ureteral reconstruction as a safe and effective alternative to open surgery, offering equivalent short-term success and reduced hospital stay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".