Portable Endoscopic Simulator for Urologic Training: A Face/Content and Construct Validity Study
Bibliographic record
Abstract
Mastering a surgical skill requires experience and repetition, yet opportunities for surgical trainees to gain real experience are variable and limited by case load. Surgical simulators have emerged in an attempt to overcome these limitations. However, the few currently available skills simulators for flexible endoscopy are costly, have limited accessibility and versatility, lack portability, and require dedicated time for practice. The use of a portable skills simulator to teach flexible endoscopy may provide a feasible alternative. This study introduces a novel, low-cost, portable, endoscopic simulation system for training basic endoscopic skills. Using custom software, the simulator presents a virtual environment featuring 3D models of anatomy, endoscopes, and endoscopic tools. The virtual endoscope and its tools are directly controlled in the simulation by motion input from a custom-manufactured portable endoscopic controller that communicates data via a Bluetooth interface. This two-part study presents proof of concept and initial pilot data examining the face/content validity and preliminary construct validity of the portable endoscopic simulator. In part 1, experts ( n = 2) and novices ( n = 6) provided ratings of fidelity and utility as a training tool. In part 2, experts ( n = 4) and novices ( n = 4) completed 10 simulated sequential basic endoscopic tasks, and time to completion was assessed. Findings indicate that the simulator has good utility as a training tool, but some features require modification to be more realistic. Furthermore, both novices and experts improved on the task with repeated measurements ( p < 0.001), but there were no significant differences between experts and novices in time to completion. Although more robust validation is required, this simulator appears promising as a feasible and cost-effective tool for providing simulation training on basic endoscopic 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 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 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".