THE EFFECT OF BENCH MODEL FIDELITY ON ENDOUROLOGICAL SKILLS: A RANDOMIZED CONTROLLED STUDY
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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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".