Development and pilot evaluation of a virtual reality simulator for HDR prostate brachytherapy
Bibliographic record
Abstract
Purpose: To develop a virtual reality simulator for high dose rate prostate brachytherapy and to test whether participation is associated with immediate gains in self-reported confidence across predefined procedural domains in two cohorts. Methods: Two modules were developed and implemented using Unreal Engine: patient preparation and template guided needle insertion. Oncology staff and trainees completed pre and post surveys that assessed confidence for recalling steps, explaining steps, identifying equipment, and explaining equipment function. Studies were conducted at the Hands On Brachytherapy Workshop (HOWBT) in London, Ontario, and at Sunnybrook Odette Cancer Centre in Toronto, Ontario. Paired Wilcoxon signed rank tests with two-sided p values compared before and after scores within each module. Results: Patient preparation (N=11) confidence increased for recalling steps (W=65, p=0.002), explaining steps (W=51, p = 0.023), identifying equipment (W=65, p=0.002), and explaining equipment function (W=60, p=0.0078). Needle insertion (N=27) confidence increased for recalling steps (W=292, p<0.001), explaining steps (W=347, p<0.001), identifying equipment (W=355, p<0.001), and explaining equipment function (W=354, p<0.001). Conclusion: The simulator was feasible to deploy and was associated with higher self-reported confidence across key domains immediately after training. Findings may inform future curriculum design and implementation work.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".