MétaCan
Menu
Retour à la cohorte
Enregistrement W2019901454 · doi:10.1111/j.1365-2929.2005.02288.x

Integration of ultrasound in the education programme in anatomy

2005· article· en· W2019901454 sur OpenAlexaff
Eli Tumba Tshibwabwa, Hallie Groves

Notice bibliographique

RevueMedical Education · 2005
Typearticle
Langueen
DomaineMedicine
ThématiqueUltrasound in Clinical Applications
Établissements canadiensMcMaster University Medical Centre
Organismes subventionnairesnon disponible
Mots-clésHuman anatomyContext (archaeology)MedicineUltrasoundSurface anatomySession (web analytics)Medical physicsRadiologyAnatomyMedical educationComputer science

Résumé

récupéré en direct d'OpenAlex

Context and setting Medical students invest a significant amount of time studying human structure using a variety of resources. They often find it challenging to visualise the functioning living human and to apply their learning clinically. Because ultrasound imaging provides a means of visualising structure and movement in a living person, it was proposed that it could be used to enhance and facilitate the learning of functional clinical anatomy. An ultrasound learning resource was located within the anatomy area to enable students to integrate their learning through ultrasound with the study of specimens and other resources. Why the idea was necessary The purpose of the study was to determine the effect on student learning of integrated ultrasound anatomy teaching sessions. These sessions included an introduction to the technology of ultrasound imaging and the hands-on experience of performing an examination in order to provide students with the opportunity to develop basic imaging skills, and to enhance their knowledge of surface and visceral anatomy. What was done Groups of 6 first-year medical students had an integrated clinical skills, anatomy and radiology session, followed by a series of 3 90-minute ultrasound anatomy sessions provided by a radiologist during the cardiovascular and renal subunits. The sessions included an introduction to the use of the ultrasound imaging, the setting of learning objectives, guidance on the location of landmarks and the visualisation of organs and tissues, a hands-on experience conducting an ultrasound examination on a living human, a discussion of its clinical relevance and an evaluation of the session. The objectives of the examinations were to use various approaches and scans to visualise and identify specific cardiac structures, major abdominal vessels, carotid arteries and the kidneys, as well as noting the relationships of adjacent structures. Videos of related pathologies were also discussed. The students were evaluated on their ability to perform an ultrasound examination at the end of the first and the last sessions, and received formative evaluation. For this study, a 5-point scale was used: from 1 = poor (inadequate identification of ‘acoustic window’ required for visualisation of prioritised structures) to 5 = excellent (ability to detect normal and pathological surface anatomy and functional morphology on reference standards). Approximately 490 medical students from 4 consecutive classes participated. The average score at the end of the first session was 3.25 ± 0.12, compared to the last session (4.15 ± 0.16; P < 0.005). The scores from this study were not provided to the students or incorporated into their formal evaluation. The student evaluations of the sessions were consistently excellent (score of 5/5), and included highly positive comments. Evaluation of results and impact The results of this study show that the small-group problem-based ultrasound anatomy sessions for students in the Medical Program at McMaster University, which focuses on small-group problem-based active learning, are a highly effective method for facilitating student learning and significantly enhance knowledge of living clinical anatomy. In addition, they provide students with a basic understanding of ultrasound imaging and its use in clinical practice. Finally, the students value these innovative sessions highly and find that this use of ultrasound is an exciting and engaging approach to stimulate the learning of ‘living’ clinical anatomy and enhance their clinical reasoning skills.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,046

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,011
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,006
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0140,002

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.

Tête enseignante Opus0,025
Tête enseignante GPT0,407
Écart entre enseignants0,382 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations119
Publié2005
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueMedical EducationMême sujetUltrasound in Clinical ApplicationsTravaux en français237 207