Bibliographic record
Abstract
I n this edition of the CUAJ, Deyirmendjian et al describe their participant and proctor experience at a two-day anatomical endoscopic enucleation of the prostate (AEEP) masterclass, highlighting novel integration of 3D-printed prostate models. 1 Despite AEEP techniques demonstrating durable and effective outcomes for the past 20 years, the historically discouraging, "steep learning curve," has only recently become an exciting area of training-focused research.Various mentorship and masterclass programs, stepwise surgical techniques, and objective serial measurements, including enucleation efficiency (g/min), all aim to flatten the learning curve and safely disseminate the use of this guideline-recommended approach.2,3 The last five years has provided a collision of increased benign prostate hyperplasia (BPH) simulator interest with the rise in 3D-printing technologies used to create anatomic prostate models.4 Personal experience with three unique prostate enucleation models highlights a wide range in available products, with model improvements occurring efficiently and effectively through provider and laboratory collaboration.Some models have focused on defining the plane between adenoma and capsule to guide trainees on staying within the correct plane, while others have focused on tissue consistency to mimic the true force required to make that difficult apical turn on a 300 mL gland.As highlighted in this publication, there remains significant room for improvement in BPH models' ability to mimic clinical bleeding, although active bleeding and achieving hemostasis has been introduced in prostate cancer models used for radical prostatectomy training and could be adapted for AEEP models.4 To date, there is certainly no evidence to declare a winning prostate model over another, although continuing to seek improvements and correlation of model training outcomes to both clinical practice outcomes and AEEP provider uptake will help guide the way.As touched upon, tracking objective measures, like enucleation and morcellation efficiency over time, has been correlated with surgeon enucleation experience, and further
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.105 | 0.017 |
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".