Learning Curves in Urolithiasis Surgery: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Procedures for urolithiasis are a core part of the development for the urologist in training. Understanding the learning curve of the procedures is important, allowing for planning in the training and assessment of trainees. The aim of this study was to systematically review the literature pertaining to learning curves in urolithiasis surgery. MATERIALS AND METHODS: The review was registered on the PROSPERO database and conducted in keeping with the Preferred Reporting Items for Systematic reviews and Meta-Analysis statement. Embase, MEDLINE, and PsycINFO were systematically searched from inception to January 2018, with a reference review conducted. All empirical studies on learning curves in urolithiasis surgery were included irrespective of procedure. Articles describing pediatric surgery, nontechnical skills in surgery, or those not written in English were excluded. RESULTS: Of 390 articles identified from screening, a final 18 studies were included. Fourteen studies identified the learning curve in percutaneous nephrolithotomy. These studies identified a learning curve of between 30 and 60 cases for both operative time (OT) and complication rates. Four articles focused on flexible ureteroscopy (FURS); the learning curve for FURS has been outlined as 60 cases for OT and 56 cases for fragmentation efficacy. CONCLUSIONS: The complexities of determining learning curves are extensive; studies use different parameters to measure outcomes and observe skill acquisition rates of surgeons with differing prior experience. Evidence in this article can guide trainee urologists with regard to the expected rate of progress. Multi-operator multicenter research utilizing standard outcome measures should be conducted to establish definitive learning curves.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.001 | 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".