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Enregistrement W4223596804 · doi:10.1097/rct.0000000000001306

Acing the Fundamentals of Radiology: An Online Series for Medical Students and Interns

2022· article· en· W4223596804 sur OpenAlexaff

Notice bibliographique

RevueJournal of Computer Assisted Tomography · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueRadiology practices and education
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésModalitiesMedical imagingTherapeutic modalitiesTreatment modalityMedical school

Résumé

récupéré en direct d'OpenAlex

PURPOSE: The current undergraduate radiology education predominantly integrates radiology with other disciplines during preclerkship years and is often taught by nonradiologists. Early exposure to radiology and profound understanding of scientific fundamentals of imaging modalities and techniques are essential for a better understanding and interest in the specialty. Furthermore, the COVID-19 pandemic-related impact on in-person medical education aggravated the need for alternative virtual teaching initiatives to provide essential knowledge to medical students. METHODS: The authors designed an online 7-session course on the principles of imaging modalities for medical students and fresh graduates in the United States and abroad. The course was delivered online and taught by radiologists from different US institutions. Pretests and posttests were delivered before and after each session, respectively, to assess change in knowledge. At the end of the course, a survey was distributed among students to collect their assessment and feedback. RESULTS: A total of 162 students and interns initially enrolled in the program by completing a sign-up interest form. An average of 65 participants attended each live session, with the highest attendance being 93 live attendees. An average of 44 attendees completed both the pretest and posttest for each session. There was a statistically significant increase in posttest scores compared with pretest scores ( P < 0.01) for each session; on average, the posttest scores were 48% higher than the pretest scores. A total of 84 participants answered the end-of-course survey. A total of 11% of the respondents described themselves as first year, 17% as second year, 18% as third year, 21% as fourth year, and 33% as "other." Attendees were enrolled in medical schools across 21 different countries with 35% of the respondents studying medicine in the United States. More than 76% of the respondents stated that they "strongly agree" that the program increased their understanding of radiology, increased their interest in radiology, and would be useful in their clinical practice in the future. Eighty-three percent of the respondents stated that they "strongly agree" that "this course was a worthwhile experience." Particularly, more than 84% of the respondents stated that among the most important components in enhancing their understanding of radiology were "the interpretation of normal imaging" and "interpretation of clinical cases." Ninety-two percent of the respondents stated that "the amount of effort to complete the requirements for this program was just right." Participants were also asked to rate each of the 8 sessions using the following scale: poor = 1 point, fair = 2, good = 3, and excellent = 4. The average rating for all 8 sessions was 3.61 points (SD = 0.55), which translates to 96% of the sessions being rated good or excellent. Eighty percent of the participants reported that the topics presented in the program were "excellent and clinically important to learn," and 20% of the participants reported that the topics presented were "good and somewhat important to learn." The participants were asked to evaluate their confidence regarding basic radiology skills before and after the program using the following scale: not confident at all = 1 point, somewhat confident = 2, moderately confident = 3, and very confident = 4. Figure 2 summarizes the responses of the participants. CONCLUSIONS: An online course to teach the fundamentals of imaging modalities could be delivered through a webinar format to medical students and interns in several countries to address the potential gaps in radiology education, therefore increasing their understanding of the different imaging modalities and their proper use in medicine.

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)0,002
score de la tête « metaresearch » (Gemma)0,004
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: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,122

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0360,010

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 Opus0,046
Tête enseignante GPT0,370
Écart entre enseignants0,324 · 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
GenreAutre

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

En bref

Citations4
Publié2022
Routes d'admission1
Résumé présentoui

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