Simulation-based prostate enucleation training: Initial experience using 3D-printed organ phantoms
Bibliographic record
Abstract
INTRODUCTION: Anatomical endoscopic enucleation of the prostate (AEEP) is an effective treatment for benign prostatic hyperplasia (BPH); however, there is controversy regarding the difficulty of learning such a technique. Simulation-based training can mimic real-life surgeries and help surgeons develop skills they can transfer to the operating room, thereby improving patient safety. This study aimed to evaluate the validity of a novel organ phantom for use in AEEP simulation training. METHODS: Participants performed AEEP on organ phantom simulators during a Masterclass using one of three energy modalities: holmium:YAG laser, thulium fiber laser, or bipolar energy. The organ phantom is composed of hydrogels and uses 3D molds to recreate prostatic tissue. Participants completed a questionnaire assessing content validity, face validity, feasibility, and acceptability of using the prostate organ phantom. RESULTS: The novice group consisted of 13 urologists. The median number of AEEP previously performed was 0 (interquartile range [IQR] 0-2). Two experts in AEEP (surgeons having performed over 100 AEEP interventions) also participated. All participants agreed or strongly agreed that there is a role for simulators in AEEP training. Participants positively rated the overall operative experience (7.3/10). Morcellation (4.7/10) and hemostasis (3.1/10) were deemed the least realistic steps. All participants considered it feasible to incorporate this organ phantom into training programs and 92.9% agreed that it teaches skills transferrable to the operating room. CONCLUSIONS: This study has established content and face validity for AEEP with three different energy sources for an organ phantom. Participants considered its use both feasible and appropriate for AEEP training purposes.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".