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Record W7117776669 · doi:10.2196/82717

First Implementation of a Point-of-Care Ultrasound Course in Undergraduate Medical Students in Peru: Mixed Methods Study

2025· article· en· W7117776669 on OpenAlexvenueno aff
Otto Barnaby Guillén-López

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCourse (navigation)Point of care ultrasoundPoint (geometry)Educational measurementCourse evaluationUltrasonography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.591
Teacher spread0.518 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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