ReviewAsian journal of urology2023
Education and training evolution in urolithiasis: A perspective from European School of Urology.
Review in Asian journal of urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Can AI and predictive models accurately predict stone-free status? a systematic review and meta-analysis.The Canadian journal of urology · 2026Pooled it
- Tips and tricks for reducing radiation exposure during retrograde intrarenal surgery: A literature review by the European Association of Urology Section of Endourology.Asian journal of urology · 2026Review
- Utility of a novel and cost-effective biological model for percutaneous renal access training.Translational andrology and urology · 2025Article
- Artificial intelligence, extended reality, and emerging AI-XR integrations in medical education.Frontiers in digital health · 2025Review
- Simulators in urology resident's training in retrograde intrarenal surgery.Acta cirurgica brasileira · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: With the worldwide increase in urolithiasis prevalence, the present study aimed to delineate and summarise recent evolutions in training for the management of urolithiasis. Methods: A PubMed search using the key words "simulation", "training", "ureteroscopy", "RIRS", "URS", "percutaneous nephrolithotomy", "PCNL", "virtual reality", "augmented reality", "artificial intelligence", "healthcare", "curriculum", and "assessment" was used to examine how education and training in urolithiasis have adapted over recent years. Focus was placed on the role of high- and low-fidelity simulation models, virtual reality and artificial intelligence, and standardised assessment and curriculum. Results: This review supports the necessity to incorporate technology, simulation, and other skill enhancement training modalities into surgical training. However, these cannot solely replace mentored training with an experienced professional supervisor. Whilst technological and simulation advancements are likely to prove increasingly important in urolithiasis training, it is just as important for stratification of robust curricula with validated assessment. We also propose a pathway for future training. Conclusion: Endourology subspeciality training programmes have successfully incorporated simulation model-based training for skill acquisition, refinement, and improving operative outcomes. Success is achieved by maintaining a delicate balance between machine and in person mentor-based training. A trainee-centred model that follows the proposed curriculum could aid this balance for the future generation of trainees.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.