Cadaveric versus Radiology Anatomy: Do we have the right balance to prepare medical students to be physicians?
Notice bibliographique
Résumé
Background Radiology is one of the cornerstones of modern medicine, playing an important role in patient diagnosis and management. All physicians, regardless of their speciality, are now expected to provide preliminary interpretations of radiological studies throughout their careers. Many medical undergraduate anatomy programs are beginning to teach radiological concepts through their anatomy curricula to improve the clinical relevance of teaching and to better prepare students for clinical practice. However, there has been no research to date assessing the optimal balance between cadaveric and radiological anatomy. The purpose of this study is to assess medical students' comfort level in identifying normal cadaveric and radiological anatomy and to identify areas where curricula can be renewed. Methods An anonymous online survey was administered to the second, third and fourth year undergraduate medical students at a large distributed medical school which teaches both cadaveric and radiologic anatomy (N = 850). The survey collected respondents' demographic information as well as prior radiology exposure. Students were asked to rank their comfort level in identifying anatomy in cadavers and on radiology studies on a 4‐point Likert scale. Statistical analysis was carried out to determine if there was any difference in student responses based on year of training (i.e. pre‐clinical vs clinical) and the Copeland score method was used to generate a rank order list of student comfort level across all variables. Results 153 students completed the survey yielding a response rate of 18%. 50.3% of respondents were female and most respondents were between 18–24 years old. 51.6% of respondents were in their clinical years (i.e. year 3 and 4) and 48.4% were in their pre‐clinical years. Most (90.8%) students reported receiving no radiology teaching prior to medical school. Most students (65.4%) reported their overall anatomy knowledge as “average.” When Likert data was used to generate a rank‐order list, students reported feeling most comfortable when identifying cadaveric organ anatomy, cadaveric bony anatomy and recognizing spatial relationships in cadavers and least comfortable when identifying radiological pathology, radiological neuroanatomy, and recognizing spatial relationships on imaging studies. There was no statistical difference in response when considering students' level of training. Conclusions Overall, medical students reported feeling less comfortable when identifying normal anatomy on radiological studies when compared to identifying the same structures in the cadaver. This study suggests medical students would benefit from increased exposure to radiology anatomy during medical school, especially given that most physicians will be primarily examining radiology images throughout their careers. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».