First Implementation of a Point-of-Care Ultrasound Course in Undergraduate Medical Students in Peru: Mixed Methods Study
Notice bibliographique
Résumé
Background: Point-of-care ultrasound (POCUS) is a test performed by physicians, as an adjunct to physical examination, to identify the presence or absence of specific clinical findings. This skill is not currently included in undergraduate medical education in Peru. Objective: This study aims to describe and evaluate the implementation of a POCUS course in undergraduate medical students. Methods: A pre-experimental study, without a control or comparison group, in which a pretest and posttest were used to evaluate the same group of students. A theoretical-practical POCUS course was designed and implemented for fifth-year medical students at the Universidad Peruana Cayetano Heredia in Lima (Peru) during late 2019 and early 2020. Their prior knowledge was assessed using a pretest consisting of 10 short-answer questions. At the end of the course, a posttest consisting of 9 different questions on ultrasound image analysis and recognition was administered, and the same 10 pretest questions were also re-evaluated. Satisfaction and perception of learning were also assessed through a survey. A descriptive analysis was performed, obtaining absolute and relative frequencies. The Wilcoxon test for related samples was used to evaluate the differences between the pretest and posttest. Results: A total of 26 students participated in the course, although only 19 completed the post-test (10 women and 9 men). The average pretest score before the course started was 4.8 (SD 2.2) points, indicating poor prior knowledge. This average increased to 18.5 (SD 1.6) points when they retested the pretest at the end of the course. The average posttest score was 12.2 (SD 3.3) points, which differed significantly from the initial pretest average (P<.001). Only 15 students responded to the satisfaction survey, with more than 50% reporting that they had fully acquired the ability to assess the inferior vena cava, bladder, free fluid in the thorax and abdomen, and right kidney. They also reported that the course met 97.5% of their prior expectations, but all considered the practical sessions with the ultrasound equipment to be essential. Although they considered that the best aspects of the course were learning how to use the ultrasound equipment and the small size of the groups, they suggested that the course could be improved by increasing its duration and the number of practical sessions, as well as by conducting the practical sessions with real patients presenting some type of pathology. Conclusions: We have successfully created a short theoretical and practical course on POCUS and have applied it for the first time to undergraduate medical students after their clinical rotations. This course has enabled them to perceive a significant improvement in their ability to recognize certain abdominal and pelvic organs and anatomical structures using ultrasound. This course can serve as a starting point for replicating POCUS teaching in medical schools across the country.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».