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Record W2891864146 · doi:10.7759/cureus.3320

Can You Teach Yourself Point-of-care Ultrasound to a Level of Clinical Competency? Evaluation of a Self-directed Simulation-based Training Program

2018· article· en· W2891864146 on OpenAlexaffabout
Fraser D Mackay, Felix Zhou, David Lewis, Jacqueline Fraser, Paul Atkinson

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMemorial University of NewfoundlandSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsMedicinePoint of care ultrasoundSession (web analytics)CertificationModalitiesMedical physicsUltrasoundMedical educationRadiologyComputer science

Abstract

fetched live from OpenAlex

Introduction Self-directed learning in medical professions is established as an effective method of training in certain modalities. Furthermore, simulation technology is becoming widely used and accepted as a valid method of training for various medical skills, with ultrasound being one of the best studied. The use of point-of-care ultrasound (PoCUS) in the practice of emergency medicine is well established, and PoCUS is a core competency of the Royal College of Physicians and Surgeons of Canada emergency medicine standards. The primary goal of our study was to assess the effectiveness of a self-directed simulation-based training program for medical students, in terms of achieving competency in basic PoCUS scans. Methods Fourteen second-year medical students with no prior ultrasound experience were provided access to online study modules created by SonoSim ultrasound training solutions (SonoSim, Santa Monica, CA, US), covering ultrasound theory and methodology, and attended a two-hour introductory session where they were introduced to the study protocol, simulation equipment, and software. Participants then undertook self-directed ultrasound simulation training throughout the year, using the CAE Vimedix PoCUS simulator (CAE Healthcare, Sarasota, FL, US) and the SonoSim ultrasound training solution system. Upon reaching 10 (and 25) scans in each of the four categories (cardiac, abdomen, aorta, and pelvic), a triggered assessment was arranged in which participants scanned a live volunteer under the direct supervision of PoCUS-certified physicians. The physicians scored the participant attempts in terms of image acquisition, interpretation, and clinical understanding. No feedback was provided to the participants. Following the study, participants submitted feedback regarding the design of the study and were asked to rank their preferred training program protocols out of a provided list of five different options. Results At the first triggered assessment (after completing only 10 scans in each category), four out of 14 participants were scored as competent in the aorta scan, two out of 14 participants were competent in the pelvic scan, and none of the participants were competent in both the cardiac and abdominal scans. Only nine out of 14 participants completed the second triggered assessment (after completing 25 scans in each category). At the second assessment, only three participants were scored as competent in the aorta scan, two participants were competent in the cardiac scan, and one participant was competent in the pelvic scan. None of the 14 learners completed the final phase of the training and assessment protocol. Feedback following the termination of the study showed that none of the participants supported continuing the study protocol as designed originally, and the preferred study design consisted of a full-day introductory course with live models and simulation, followed by self-directed learning with simulation and live models until 50 scans in each category were achieved. Conclusion We were unable to demonstrate the achievement of competence in PoCUS in medical learners engaged in our combined self-directed simulation-based training program. This is in contrast to the considerable literature supporting self-directed learning and simulation-based learning for other skills. Feedback from faculty, curriculum integration, and alignment with clinical experience may be beneficial.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.271
GPT teacher head0.490
Teacher spread0.220 · 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 teacher head, not a consensus.

Study designObservational
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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Citations22
Published2018
Admission routes2
Has abstractyes

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