Randomized prospective blinded study validating acquistion of ureteroscopy skills using computer based virtual reality endourological simulator.
Bibliographic record
Abstract
PURPOSE: Surgical simulation has emerged in the last decade as a potential tool for aiding acquisition of technical skills, including anesthesia protocols, trauma management, cardiac catheterization and laparoscopy. We evaluate and validate the use of a computer based ureteroscopy simulator (URO Mentor, Simbionix Ltd., Lod, Israel) in the acquisition of basic ureteroscopic skills. MATERIALS AND METHODS: We assessed 20 novice trainees for the ability to perform basic ureteroscopic tasks on a computer based ureteroscopy simulator. Participants were randomized to receive individualized mentored instruction or no additional training, and subsequently underwent post-testing. Pre-training and post-training improvement in performance was assessed by objective simulator based measurements. Subjective overall performance was rated using a validated endourological global rating scale by an observer blinded to subject training status. RESULTS: Demographics and pre-test scores were similar between groups. Post-testing revealed a significant effect of training on objective and subjective measurements. Spearman rank correlation demonstrated a significant association between objective simulator based measurements and the endourological global rating scale. CONCLUSIONS: Use of a computer based ureteroscopy simulator resulted in rapid acquisition of ureteroscopic skills in trainees with no prior surgical training. Results of this study demonstrate the use of a virtual reality ureteroscopy simulator in endourological training. Correlation of simulator based measurements with a previously validated endourological global rating scale provides initial validation of the ureteroscopy simulator for the assessment of ureteroscopic skills.
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 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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".