Computer Enhanced Visual Learning Method to Train Urology Residents in Pediatric Orchiopexy Provided a Consistent Learning Experience in a Multi-Institutional Trial
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».