Bibliographic record
Abstract
Introduction: Mastering a surgical skill requires experience and repetition, yet opportunities for surgical trainees to gain real experience is variable and limited by case load.Surgical simulators have emerged to attempt to overcome these limitations.In urology, few commercially available simulators exist.One that has seen limited adoption, the URO Mentor, has been validated in several studies.However, this system is expensive, at $60 000 USD, and is not portable.The goal of this work is to develop and validate a lowcost, portable endoscopic simulation system for training urology residents.Methods: We developed a system that simulates the experience of endoscopy in urology.The system consists of a smartphone/tablet application (app) that displays an endoscopic camera view, and a wireless controller modelled like a real endoscope (Figs. 1, 2).The app is designed like a game, with sequential levels adding complexity to tasks that must be completed.This initial study focuses on face validation of the prototype.Post-participation surveys were administered to urology residents and staff and a five-point Likert scale was used to evaluate simulator's realism and usefulness.Results: Subjective scores were obtained relating to the look, feel, and physics of the system, and a global score rating the system's perceived utility in increasing residents' performance in real life.All users rated at least 4/5 in all domains of usefulness as a teaching tool and reasonable scores for realism (Figs. 3, 4) Conclusions: We have created a portable endoscopic simulation system for training urology residents.In this phase of our study, we obtained feedback that will inform the next iteration of the system.This validation is essential to the next phase, which will quantify the system's ability to improve resident real-world performance.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.197 | 0.097 |
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".