Learning Curves in Pediatric Robot-Assisted Pyeloplasty: A Systematic Review
Bibliographic record
Abstract
Background: Robot-assisted surgery demands a specific skillset of surgical knowledge, skills, and attitudes from the robotic surgeon to function as part of the robotic team and for maximal utility of the assistive surgical robot. Subsequently, the learning process of robot-assisted surgery entails new modes of learning. We sought to systematically summarize the published data on pediatric robot-assisted pyeloplasty (pRALP) to decipher the learning process by analyzing learning curves. Methods: This review followed the PRISMA guidelines. PubMed, EMBASE, Web of Science, and Scopus databases were systematically searched for 'learning curve' AND 'pediatric pyeloplasty'. All studies presenting outcomes of learning curves (LC) in the context of pRALP in patients < 18 years of age were included. Studies comparing LC in pRALP versus open and/or laparoscopic pyeloplasty were also included; however, those solely focusing on LC in non-robotic approaches were excluded. The methodological quality was assessed using the Newcastle and Ottawa scale. Results: Competency was non-uniformly defined in all fifteen studies addressing learning curves in pRALP. pRALP was considered safe at all stages. Proficiency in pRALP was reached after 18 cases, while competency was estimated to demand 31 operated cases with operative duration as outcome variable. Conclusions: Pediatric RALP is safe during the learning process and 'learning by doing' improves efficiency. Competencies with broader implications than time must be defined for future studies.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".