A high-fidelity, virtual reality, transurethral resection of bladder tumor simulator: Validation as a tool for training
Bibliographic record
Abstract
INTRODUCTION: Simulation-based training is used to help trainees learn surgical procedures in a safe environment. The objective of our study was to test the face, content, and construct validity of the transurethral resection of bladder tumor (TURBT) module built on the Simbionix TURP Mentor simulator. METHODS: Participants performed five standardized cases on the simulator. Domains of the simulator were evaluated on a five-point Likert scale to establish face and content validity. Construct validity was assessed through the simulator's built-in scoring metrics, as well as video recordings of the simulator screen and an anonymized view of participants' hands and feet, which were evaluated using an objective structured assessment of technical skills (OSATS) tool. RESULTS: Ten experienced operators and 15 novices participated. Face validity was somewhat acceptable (mean realism 3.8/5±1.03 standard deviation [SD]; mean appearance 4.1/5±0.57), as was content validity, represented by simulation of key steps (mean 3.9±0.57). The simulator failed to achieve construct validity. There was no difference in mean simulator scores or OSATS scoring between experienced operators and novices. Novices significantly improved their mean simulator scores (305.9 vs. 332.4, p=0.006) and OSATS scoring (15.8 vs. 18.1, p=0.001), while 87% felt their confidence to perform TURBT improved. Overall, 92% of participants agreed that the simulator should be incorporated into residency training. CONCLUSIONS: Our study suggests a role for the TURBT module of the Simbionix TURP Mentor simulator as an introduction to TURBT for urology trainees. Strong support was found from both experienced operators and novices for its formal inclusion in resident education.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".