THE EFFECT OF BENCH MODEL FIDELITY ON ENDOUROLOGICAL SKILLS: A RANDOMIZED CONTROLLED STUDY
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Complex skills, such as ureteroscopy and stone extraction, are increasingly taught to novice urology trainees using bench models in surgical skills laboratories. We determined whether hands-on training improved the performance of novices more than those taught only by a didactic session and whether there was a difference in the performance of subjects taught on a low versus a high fidelity model. MATERIALS AND METHODS: We randomized 40 final year medical students to a didactic session or 1 of 2 hands-on training groups involving low or high fidelity bench model practice. Training sessions were supervised by experienced endourologists. Testing involved removal of a mid ureteral stone using a semirigid ureteroscope and a basket. Blinded examiners tested subjects before and after training. Performance was measured by a global rating scale, checklist, pass rating and time needed to complete the task. RESULTS: There was a significant effect of hands-on training on endourological performance (p <0.01). With respect to bench model fidelity the low fidelity group did significantly better than the didactic group (p <0.05). However, no significant difference was found between the high and low fidelity groups (p >0.05). The low fidelity model cost Canadian $20 to produce, while the high fidelity model cost Canadian $3,700 to purchase. CONCLUSIONS: Hands-on training using bench models can be successful for teaching novices complex endourological skills. A low fidelity bench model is a more cost-effective means of teaching ureteroscopic skills to novices than a high fidelity model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it