Virtual Dissection Adds Educational Value to a Traditional Medical Undergraduate Cadaveric Anatomy Course
Notice bibliographique
Résumé
Background Virtual dissection is performed on near life‐size anatomy visualization tables (AVTs), which are like hospital radiology workstations. Patient CT scans are loaded into these tables and through powerful software interactions students work together to manipulate the data and perform their dissection. The purpose of this study was to develop virtual dissection laboratories for first year medical students and to qualitatively assess the educational value of these sessions as well as students' preferred pedagogical approaches for this new technology. Methods All students in the first‐year medical undergraduate program were included in this study (n = 292). A Basic Virtual Dissection Curriculum focused on normal anatomy was offered to all students concurrently with the Cadaveric Laboratory Sessions and an Advanced Virtual Dissection Curriculum focused on pathology was offered as 4 extra‐curricular sessions. 36.6% of students participated in the Advanced Curriculum. Following the course, both groups of students were surveyed to determine their attitude toward virtual dissection and the pedagogical approaches they perceived to be the most useful for this technology. Results were statistically analyzed using the Schulze method. Results The response rate for the Basic Curriculum was 69.2% and the response rate for the Advanced Curriculum was 82.9%. 93% indicated that virtual dissection was “definitely” a valuable addition to the anatomy lab. 89% of respondents “agreed” or “strongly agreed” that AVT virtual dissection improved their understanding of disease and pathology. They reported that the aortic aneurysm case was the most memorable case because the imaging made it easier to understand the pathogenesis of the disease. Students felt that small group demonstration and problem‐based learning would be the best teaching approaches for this technology. Conclusions Virtual dissection adds educational value to undergraduate anatomy teaching, primarily because it provides students with a clinical context for the anatomy they are learning. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0,000 | 0,000 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».