Learning curve of laparoscopic nephrectomy: a prospective pilot study
Bibliographic record
Abstract
Abstract Background Learning curve of laparoscopic nephrectomy (LN) is mainly affected by two main factors: plotting performance and experience. However, there is paucity in the literature addressing the number of cases required to adopt LN. Herein, we aimed to assess the learning curve of LN for various renal disorders and number of cases required to adopt the technique. Between September 2015 and December 2017, consecutive patients undergoing LN for various renal diseases were enrolled in this study. Patients were divided into two groups, the first 20 cases (group A) and subsequent 20 cases (group B). All procedures were performed by a single trainee urologist under supervision of an expert endourologist. Learning curve was assessed using operative time and incidence of complications. Results A total of 40 patients were included in this pilot clinical study. Mean age was 38.2 ± 16.3 years. The mean operative time for patients in group B was significantly lower than the mean operative time for patients in group A (108.5 vs. 139.3 min, p < 0.05). However, there were no significant differences between both groups in terms of intraoperative blood loss (86 vs. 104 ml; p = 0.081), conversion to open surgery (5% vs. 10%; p = 0.256) and postoperative complications (5% vs. 15%; p = 0.09) for group B and group A, respectively. Similarly, there was no significant difference between both groups in terms of hospital stay (42 ± 8 vs. 46 ± 11 h p = 0.01). The trainee surgeon reached a plateau after 22 cases. Conclusions Our study suggests that a minimum of 22 LN procedures are needed in order to adopt the technique of laparoscopic nephrectomy. Learning curve of LN is mainly affected by number of performed procedures within a short period of time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".