Computer Enhanced Visual Learning Method to Train Urology Residents in Pediatric Orchiopexy Provided a Consistent Learning Experience in a Multi-Institutional Trial
Bibliographic record
Abstract
PURPOSE: Computer enhanced visual learning is a new method to train residents to perform surgery using components and provide them with access to a personalized surgical feedback archive using the Internet. At the parent institution in Chicago we have already noted that this method is effective to train residents to perform orchiopexy. To assess whether this new methodology to enhance resident surgical instruction is generalizable we performed a prospective, multi-institutional clinical trial. MATERIALS AND METHODS: We prospectively compared ratings of resident skills in performing pediatric orchiopexy at 4 institutions as novices to computer enhanced visual learning curriculum (study group) vs those at the single institution accustomed to that curriculum (control group). All urology residents and attending physicians accessed the computer enhanced visual learning curriculum. After each case was completed the attending urologist rated resident performance of each step and provided feedback on weaknesses for the resident to remediate at the next case. The learning score was calculated for each case as the sum of the ratings × case difficulty. Scores on the first case and the best case were compared between the study and control groups by resident and institution. RESULTS: The study group included 6 attending physicians and 36 residents (99 orchiopexies). The control group included 8 attending physicians and 21 residents (108 orchiopexies). Between the study and control groups we noted no significant differences in average resident postgraduate year (2.9 vs 2.7), number of procedures per resident (3.9 vs 4.9), frequency with which residents viewed computer enhanced visual learning preoperatively (63% vs 74%) or attending physician provision of feedback (63% vs 88%) (each p not significant). Similarly of residents who completed more than 1 surgery there was no significant difference in the percent who showed an improved learning score in the study vs the control group (86% vs 79%) or in the magnitude of average improvement (10.5 vs 13.4) (each p not significant). CONCLUSIONS: The institutional groups did not differ in training resident skills using computer enhanced visual learning for pediatric orchiopexy. Thus, the program provides a consistent learning experience and is generalizable across institutions. We believe that this tool will change the practice of how training programs educate residents by enhancing learning by a checklist approach and a computer platform to archive feedback and remediation.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".