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Enregistrement W2055981983 · doi:10.1111/j.1365-2929.2007.02745.x

Teaching musculoskeletal ultrasound in the undergraduate medical curriculum

2007· article· en· W2055981983 sur OpenAlexaff
Eli Tumba Tshibwabwa, Hallie Groves, Mitchell Levine

Notice bibliographique

RevueMedical Education · 2007
Typearticle
Langueen
DomaineMedicine
ThématiqueMusculoskeletal Disorders and Rehabilitation
Établissements canadiensMcMaster University Medical Centre
Organismes subventionnairesnon disponible
Mots-clésCurriculumContext (archaeology)Medical educationResource (disambiguation)MedicineRadiologyMedical physicsComputer sciencePsychologyPedagogy

Résumé

récupéré en direct d'OpenAlex

Context and setting Although a wide range of resources is available to teach anatomy to medical students using the problem-based learning (PBL) approach, resources were needed to help them understand the dynamic living anatomy of the musculoskeletal (MSK) system. Using ultrasound imaging, students can visualise MSK dynamics. Its advantages include availability, non-invasiveness, quick scan-time and real-time imaging. The equipment is located in the anatomy laboratory, where students have immediate access to anatomical specimens. Why the idea was necessary The purpose was to determine the effectiveness of ultrasound as a teaching/learning tool for topographical and dynamic anatomy, and to provide an introduction to its technology and the opportunity to develop basic skills in image interpretation through independent practice. Although this educational resource is simply adjunctive, it is not commonly used to teach MSK anatomy. One of the difficulties in introducing a new or modified curriculum is a tendency to ask: ‘What’s extraordinary about it? What's new?' In this study, we assessed the ‘evolution’ of what may have been a good but ordinary curriculum. Although our approach may not be extraordinary, we needed data to evaluate it as it relates to the MSK curriculum in undergraduate medical education. To the best of our knowledge, no other study applying and evaluating this approach has been published. What was done Approximately 420 Year 2 medical students (19 groups of 6 students over 4 consecutive years) were involved. Staff time was not an issue as each staff member expects to teach approximately 52 hours/year (210/4). This is an acceptable workload (our faculty members devote 80 hours per unit as tutors, and as clinical preceptors a minimum of 52 hours per year for the locomotor/nervous system/brain unit). Students learn from hands-on ultrasound experience, using Toshiba SSA-220A machines with 7.5-MHz linear probes, on a normal control model. CD-ROMs of specific normal aspects and pathologies were progressively viewed and discussed. The group's ultrasound/anatomy learning objectives related to health care problems covered in the locomotor unit, primarily the assessment of the integrity and abnormalities of specific structures (shoulder, elbow, hand, hip, knee, ankle). The students negotiated how they would approach their problems, and integrated the practical applications. At the end of each session, students were evaluated on a 5-point scale by a faculty rater using standardised criteria. Students and faculty provided written feedback within the sessions. Scores were not provided to students or incorporated into their formal evaluation. Evaluation of results and impact Average scores at the end of the first and second sessions were 3.31 ± 0.11 and 4.28 ± 0.13, respectively; P > 0.005. Feedback on the tutorial sessions consistently rated them as excellent and the CD-ROMS were highly valued as a means of reinforcing learning. The results show that the use of hands-on MSK ultrasound examinations in anatomy facilitates learning, significantly enhances knowledge and understanding of the living system, and teaches students physical examination skills. In addition, not only do students acquire the skills to perform and interpret ultrasound, they also analyse and apply this knowledge to surgical, physiological and diagnostic imaging concepts. Challenging students in these different contexts is an essential component of the PBL approach.

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)0,005
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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,039

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,011
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0020,001
Science ouverte0,0010,005
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,004
Tête enseignante GPT0,333
Écart entre enseignants0,328 · 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'étudeObservationnel
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

Citations59
Publié2007
Routes d'admission1
Résumé présentoui

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