The development of laparoscopic surgical skills in pediatric urologists: longterm outcome of a mentorship-training model.
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: We previously reported the successful attainment of laparoscopic skills in a group of practicing pediatric urologists without previous formal laparoscopic training. During the mentorship period, the four urologists (trainees A, B, C, and D) performed a number of renal retroperitoneal laparoscopic procedures (RRLP) under the tutelage of an expert mentor. Specifically, trainee A performed or assisted in 8 RRLP while trainees B, C, and D performed/assisted in 10, 7, or 18 RRLP, respectively. Herein we assessed the outcome of this training program and practice pattern of this same group of urologists. METHODS: Following the completion of the mentorship period, we reviewed the outcomes of all of the consecutive RRLP performed from September 2001 to March 2005 with respect to operative time, conversion rate, perioperative complications and length of hospital stay (LOS). Furthermore, we attempted to correlate the number of procedures each surgeon performed both during and subsequent to the mentorship period. RESULTS: Fifty-two ablative RRLP including nephrectomy (n=38), partial nephrectomy (n=12), or synchronous bilateral nephrectomy (n=2), were performed on 50 patients (19 males, 31 females) with a mean age of 5.5 years (range 4 months-14 years). Trainee A performed 16/40 procedures, trainees B and C each performed 2/40, while trainee D performed 20/40 procedures. Mean operative time was 2.4 hours (range 1.5-6.3 hours). Five patients required open conversion due to inability to obtain retroperitoneal access (n=3) or failure to progress (n=2). Two patients (one nephrectomy, one partial nephrectomy) developed retroperitoneal urinomas requiring temporary urinary diversion. There were no other perioperative complications and mean LOS was 1.2 days (range 1-4 days). More advanced reconstructive procedures have since been performed with the aid of laparoscopic exposure; trainee D has thus far successfully performed 12 laparoscopically assisted pyeloplasties. CONCLUSIONS: This series demonstrates the effectiveness of the mentorship-training model to introduce RRLP to a pediatric urology training program. It is evident that the post-mentorship practice is affected by the number of cases initially performed during the training period. The development of an "expert" laparoscopist is dependent not only on initial training experience, but continued education through ongoing case exposure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".