324 The Learning Curves of Major Laparoscopic and Robotic Procedures in Urology: A Systematic Review
Bibliographic record
Abstract
Abstract Aim Urology has been at the forefront of adopting laparoscopic and robot-assisted techniques to improve patient outcomes. Defining the learning curves for these procedures enables assessment of trainee performances relative to expected progress and has important implications in research. This systematic review aimed to examine the literature relating to the learning curves of major urological robotic and laparoscopic procedures. Method In accordance with PRISMA guidelines, a systematic literature search strategy was employed across PubMed, EMBASE, and the Cochrane Library from inception to December 2021 for eligible studies alongside a search of the grey literature. Two independent reviewers completed the article screening and data extraction stages, using the Newcastle-Ottawa Scale (NOS) to then undertake quality assessment of the included articles. The review was reported in accordance with AMSTAR guidelines. Results Of 3702 records identified, 97 eligible studies were included for narrative synthesis. 39 studies evaluated robot-assisted laparoscopic prostatectomy (RALP) with the learning curve identified as 10-250 cases for operative time and 250-300 for potency. The shortest learning curve was reported for hand-assisted laparoscopic nephrectomy, involving 4-10 cases. There was considerable variation in the study designs, outcome measures and prior experience of surgeons, with all studies scoring either 5 or 6 on the NOS. Conclusions Standardised reporting of outcomes and performance measures is required to reduce heterogeneity and enable the undertaking of a meta-analysis. Future studies should use multiple surgeons and large sample sizes of cases to identify the currently undefined learning curves for laparoscopic radical cystectomy and for robotic and laparoscopic retroperitoneal lymph node dissection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".