Competence in and Learning Curve for Pediatric Renal Transplant Using Cumulative Sum Analyses
Bibliographic record
Abstract
PURPOSE: We assessed achievement of competence in pediatric renal transplant by developing a learning curve model for consecutive operations performed by a single surgeon. MATERIALS AND METHODS: We retrospectively evaluated pediatric renal transplant cases performed by an index pediatric urologist during his first 15 years of being the primary transplant surgeon at our institution. Case characteristics, operative time and surgical complications within 30 days postoperatively were assessed and compared to those of a reference senior surgeon. To generate a learning curve plot, we performed a cumulative sum analysis to evaluate the evolution of operative times and surgical complications. RESULTS: During 15 years 55 pediatric renal transplants (17%) were performed by the index surgeon and 78 (24%) by the senior surgeon. Total operative time was shorter for the index surgeon (226 vs 252 minutes, p = 0.006), while ischemia time was longer (40 vs 30 minutes, p = 0.001). The 30-day surgical complication rates were similar (32.7% and 35.9%, p = 0.853). The learning curve showed that the complication rates and operative times did not increase following the 17th case. Ureteroureterostomy has been more commonly performed for ureteral anastomosis (p = 0.048) and longer warm ischemia time has been noted after reaching the peak of the learning curve (p = 0.003). CONCLUSIONS: We determined that technical skills for pediatric renal transplant can be achieved after the 17th case. We propose that a dedicated team with a pediatric urologist who has an interest in performing pediatric renal transplant secure more cases than the case volume determined in our study within the first few years of practice to maintain proficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".