Mentored retroperitoneal laparoscopic renal surgery in children: a safe approach to learning
Bibliographic record
Abstract
Minimally invasive surgery is not exclusive to the treatment of adult conditions. It has also been used in paediatric urology, and the authors from Toronto and Paris describe a mentorship‐training model for laparoscopic retroperitoneal surgery. They confirmed that a mentored approach is the way to develop this procedure. They also found that ablative procedures are learned relatively early, but that reconstructive procedures require a high degree of skill in laparoscopic techniques, requiring formal training focusing mainly on suturing techniques. OBJECTIVE To review the feasibility of introducing advanced retroperitoneal renal laparoscopic surgery (RRLS) to a paediatric urology division, using the mentorship‐training model. Although the scope of practice in paediatric urology is currently adapting endoscopic surgery into daily practice, most paediatric urologists in North America have had no formal training in laparoscopic surgery. METHODS The study included four paediatric urologists with 3–25 years of practice; none had had any formal laparoscopic training or ever undertaken advanced RRLS. An experienced laparoscopic surgeon (the mentor) assisted the learning surgeons over a year. The initial phases of learning incorporated detailed lectures, visualization through videotapes and ‘hands‐on’ demonstration by the expert in the technique of the standardized steps for each type of surgery. Over 10 months, ablative and reconstructive RRLS was undertaken jointly by the surgeons and the mentor. After this training the surgeons operated independently. To prevent lengthy operations, conversion to open surgery was planned if there was no significant progression after 2 h of laparoscopic surgery. RESULTS Over the 10 months of mentorship, 36 RRLS procedures were undertaken in 31 patients (28 ablative and eight reconstructive). In all cases the mentored surgeons accomplished both retroperitoneal access and the creation of a working space within the cavity. The group was able to initiate ablative RRLS but the mentor undertook all the reconstructive procedures. After the mentorship period, over 10 months, 12 ablative procedures were undertaken independently, and five other attempts at RRLS failed. CONCLUSION Although the mentored approach can successfully and safely initiate advanced RRLS in a paediatric urology division, assessing the laparoscopic practice pattern after mentorship in the same group of trainees is warranted. Ablative RRLS is easier to learn for the experienced surgeon, but reconstructive procedures, e.g. pyeloplasty, require a high degree of skill in laparoscopic technique, which may only be acquired through formal training focusing primarily on suturing techniques.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".