The learning curve for pure retroperitoneoscopic donor nephrectomy by using cumulative sum analysis
Bibliographic record
Abstract
INTRODUCTION: This study aimed to identify a precise learning curve for pure retroperitoneoscopic donor nephrectomy (RDN). METHODS: Data from 172 consecutive kidney donors who underwent pure RDN between January 2010 and July 2019 were prospectively collected and evaluated. Cumulative sum (CUSUM) analysis was used for testing the operation time. Changepoints were determined by using the r program and BINSEG method. The cohort was divided into three groups - group 1: competence, including the first 10 cases; group 2: 11-48 cases as proficiency; and group 3: the subsequent 124 cases as expert level. Continuous variables were evaluated using one-way ANOVA, and categorical data were evaluated using the Chi-squared test. RESULTS: Right RDN was performed in 39 (22.7%) donors. The eighth patient was converted to open surgery due to vena cava injury and excluded from the CUSUM analysis. Depending on experience in pure RDN, a significant decrease was detected in operative time (p<0.001), warm ischemia time (p=0.006), and blood loss (p<0.001). Recipient complications and graft function were found to be statistically comparable. CONCLUSIONS: In our study, the attainment of expertise in pure RDN was observed after performing 50 cases. The transperitoneal technique, which is a feasible alternative, is far more widely used than pure RDN. We believe that understanding the learning curve associated with pure RDN could facilitate the adoption of this approach as a viable alternative to the transperitoneal approach.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".