Bibliographic record
Abstract
Introduction: Simulation-based training (SBT) is designed to mimic real-life surgeries and help surgeons develop skills they can transfer to the operating room in a risk-free environment.Within the field of urology, surgical simulators are playing an increasingly important role.In particular, with the emergence of numerous minimally invasive surgical therapies (MISTs) for benign prostatic hyperplasia (BPH), a need has developed for new learning tools in addition to standard clinical exposure.In this review, we focused on three main surgical techniques for BPH, including transurethral resection of the prostate (TURP), laser enucleation (HoLEP), and photovaporization of the prostate (PVP).Methods: We conducted a systematic review based on a prespecified protocol and used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.References were identified through searches of MEDLINE, Embase, and the Cochrane CENTRAL databases from inception through July 31, 2022.Keywords and medical subject heading terms searched included those related to simulators, medical education, and BPH surgeries.Studies included were original articles on simulators used for BPH surgery.Data was collected on each simulator used, including a description, design of simulator integration in training, and type and expertise of participants using the simulator.Simulator validity was collected, such as face, content, and construct validity.Acceptability and feasibility of integration were also assessed.Results: Among the 37 records identified, 26 studies aimed to assess the validity of prostate models for TURP simulation training, six studies for GreenLight laser prostatectomy, four studies for HoLEP procedures, and one article for THuLEP procedures.We identified only three models that were validated for face, content, construct, acceptability, and feasibility.Most models were virtual reality-based.Conclusions: Our results suggest a need for development of models other than TURP and the evaluation of feasibility and acceptability of current valid BPH surgical models.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.398 | 0.090 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".