Current Educational Interventions for Improving Technical Skills of Urology Trainees in Endourological Procedures: A Systematic Review
Bibliographic record
Abstract
Objective: Endourology continues to grow with the introduction of new technologies into clinical practice. Simulators and training models have been developed to improve comfort and proficiency in endoscopic procedures. The purpose of this systematic review was to examine the current educational interventions utilized to improve the performance of endourology trainees and to critically appraise the strengths and limitations of each. Methods: A search of the Ovid MEDLINE, EMBASE, PsycINFO, and the Cochrane Library databases was performed to identify literature focused on current educational interventions for improving technical skills of trainees in endourologic procedures. The Medical Education Research Study Quality Instrument (MERSQI) was used to evaluate the methodological quality of the abstracted articles. Results: Of the 2236 articles identified, 22 met the inclusion criteria. The types of educational interventions included: bench/wet lab models, virtual reality simulators, and instructional courses. Metrics used to quantify the impact of these interventions include global rating scales, Objective Structured Assessment of Technical Skills (OSATS) scores, and task-specific checklists. The setting of these evaluations comprises both virtual reality simulators and live surgery. Conclusions: In the surgical education literature, simulation-based training and assessment continues to play a prominent role in urologic training. The educational interventions highlighted in this review address various aspects of endourology, from stone management to transurethral resection. Additional work is needed to correlate technical performance in clinical and nonclinical settings with patient outcomes and develop a focused approach to nontechnical skill training.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".