Thiel‐embalmed cadavers as a novel training model for ultrasound‐guided supine endoscopic combined intrarenal surgery
Bibliographic record
Abstract
OBJECTIVE: To evaluate Thiel-embalmed cadavers (TEC) as a training model for percutaneous nephrolithotomy (PCNL), more specifically for ultrasound (US)-guided supine endoscopic combined intrarenal surgery (ECIRS). SUBJECTS AND METHODS: Thirteen urologists (nine experienced endourologists, four fellows/residents) performed an US-guided supine ECIRS procedure on a TEC. The model was evaluated by way of a questionnaire (5-point Likert scale). Descriptive statistical analysis was performed and results were graphically presented using divergent bar graphs. RESULTS: US images were appreciated as lifelike in all aspects. Although distention of the collecting system was not ideal in one out of three TEC, US visualisation of the distended calyces during puncture was good. Skin penetration was more difficult and less realistic in TEC, while kidney puncture and dilatation were deemed very realistic. Ureteric and collecting system anatomy and consistency were similar to real life, although the mucosa appeared paler. US needle guidance was perceived as excellent. Overall, needle puncture assessment was realistic and useful as a training tool. Overall quality and satisfaction of TEC in US-guided supine ECIRS was good to excellent and comparable to a real-life procedure. Overall appropriateness of the TEC model was considered excellent for both initial and advanced supine PCNL training. CONCLUSION: Despite the minor drawbacks of tough skin and non-ideal collecting system dilatation during ureteroscopy, the TEC model was considered good to excellent as a training model for US-guided PCNL, in particular, US-guided needle puncture of the kidney during supine ECIRS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".