Development and Validation of a Mixed-Reality Simulator for Reducing Biopsy Core Deviation During Simulated Freehand Systematic Prostate Biopsy
Bibliographic record
Abstract
INTRODUCTION: We describe the development and validation of a mixed-reality prostate biopsy (PBx) simulator with built-in guidance aids and real-time 3-dimensional visualization. METHODS: We evaluated our simulator during one-on-one training sessions with urology residents and attendings from 2018 to 2022. Participants performed freehand, side-fire, double-sextant transrectal ultrasound-guided systematic prostate biopsy (sPBx). After a baseline assessment (first set of 12 biopsy cores), participants trained for 25 minutes with visualization and cognitive aids activated. Training was followed by an exit set of 12 biopsy cores without visualization or cognitive aids and afterward, subjective assessment by trainees of the simulator. Deviation is the shortest distance of the center of a core from its intended template location. RESULTS: Baseline deviations (mean ± SD) for residents (n = 24) and attendings (n = 4) were 13.4 ± 8.9 mm and 8.5 ± 3.6 mm ( P < 0.001), respectively. Posttraining deviations were 8.7 ± 6.6 mm and 7.6 ± 3.7 mm ( P = 0.271), respectively. Deviations between baseline and exit were decreased significantly for residents ( P < 0.001) but not for attendings ( P = 0.093). Overall feedback from participants was positive. Confidence in performing a PBx increased in novices after training ( P = 0.011) and did not change among attendings ( P = 0.180). CONCLUSIONS: A new PBx simulator can quantify and improve accuracy during simulated freehand sPBx while providing visualization and graphical feedback. Improved simulated sPBx accuracy could lead to more even distribution of biopsy cores within the prostate when performed in clinical settings, possibly reducing the high risk of missing an existing lesion and thus decreasing the time to initiating treatment, if indicated.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".