Randomized prospective blinded study validating acquistion of ureteroscopy skills using computer based virtual reality endourological simulator.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it