Robotic-assisted laparoscopic sacrocolpopexy: Initial Canadian experience
Bibliographic record
Abstract
INTRODUCTION: Abdominal sacrocolpopexy provides effective long-term outcomes for apical pelvic organ prolapse. The introduction of robotic-assisted laparoscopic sacrocolpopexy (RALS) has mitigated the risks of abdominal surgery. This study aims to evaluate the preoperative patient characteristics, intraoperative surgical parameters, and postoperative outcomes of RALS, which has not been previously performed in Canada. METHODS: A retrospective chart review of 47 patients who have undergone RALS from 2016-2018 by a single surgeon at a tertiary care hospital in Canada was completed. RESULTS: , and Charlson comorbidity index of 2.0. Preoperatively, 13 (28%), 23 (49%), and 11 (23%) patients had Baden-Walker grade 2, 3, and 4 apical prolapse, respectively. Intraoperatively, 45 (96%) patients underwent concomitant procedures, including 36 (77%) with adnexal surgery, 32 (68%) with anti-incontinence surgery, and 25 (53%) with hysterectomy. Intraoperative complications included one ureteric injury, two bladder injuries, and three vaginotomies. The mean robotic console time, surgery time, and total operating room times were 125.6, 148.6, and 190.6 minutes, respectively. Postoperatively, data was analyzed for 32 (68%) patients with greater than 12 weeks' followup. There was no recurrence of apical prolapse on exam after a mean followup of 60.1 weeks. Seven (22%) patients experienced symptomatic prolapse in the posterior compartment. There were two grade 3 Clavien-Dindo complications, including osteomyelitis and mesh exposure. CONCLUSIONS: RALS can be safely and effectively performed with other pelvic procedures with good outcomes. Concurrent posterior repairs may be considered in select patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".