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Does Pre‐Clerkship physician assistant POCUS training improve Knowledge and confidence in Clerkship?

2020· article· en· W3017129262 on OpenAlexaff
Vian Mohialdin, Ari Shali, Bruce Wainman, Esai Bishop, Beata Cheung

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsWestern UniversityMcMaster University
Fundersnot available
KeywordsCurriculumMedicineModalitiesHealth careTUTORMedical educationUltrasonographyPoint of care ultrasoundMedical physicsUltrasoundRadiologyComputer sciencePsychology

Abstract

fetched live from OpenAlex

Introduction With the technological progress of different types of portable Ultrasound machines, there is a growing demand for all health care providers to perform bedside Ultrasonography, also known as Point of Care Ultrasound (POCUS). This technique is becoming extremely useful as part of the Clinical Skills/Anatomy teaching in the undergraduate Medical field curriculum. Teaching/training health care providers how to use these portable Ultrasound machines can complement their physical examination findings and help in a more accurate diagnosis, which leads to a faster diagnosis and better patient outcomes. In addition, using portable Ultrasound machines can add more safety measurements to every therapeutic/diagnostic procedure when it is done under an Ultrasound guide. Ultrasound is one of the different imaging modalities that health care providers depend on to reach their diagnosis, while also being the least invasive method Aim To assess the effect of pre‐clerkship POCUS training on their knowledge and confidence of POCUS training during their clerkship Method The research we report in this manuscript is a preliminary qualitative study. It provides the template for future models for teaching hands on Ultrasound for all health care providers in different learning institutions. The McMaster Physician Assistant program is a two‐year course; we introduce POCUS training to the first and second year Physician Assistant curriculum. We have a total of 24 Physician Assistant students at each level of the program; at each level we divide them into three equal groups, supervised by a tutor. Each group uses one portable General Electric Ultrasound machine, which is projected onto a large plasma screen. We dim the room lights to get better quality screen images. Our session lasts for 90 minutes, the first 20 minutes being an introduction to how to use the machine and probe orientation, as well as some anatomy landmarks. Every student will have the chance to scan their peers at least one time during our session. Our objective is a pure “hands on” scanning of the neck and the abdomen performed by the students. With the correlations to their anatomy background knowledge, they were able to identify normal Thyroid Glands and major neck vessels, Liver, abdominal Aorta, inferior vena cava, Gall Bladder, and the Kidneys. Result A questionnaire was handed to the second year (clerkship) Physician Assistant students to evaluate their hands on ultrasound session experience. And the effect of their previous POCUS training at pre‐clerkship level in enhancing more confidence on their most recent training. Answers were collected and data was analyzed into multiple graphs (as illustrated in this poster). Discussion and Conclusion These results illustrate the importance of the prior POCUS training for Physician Assistant students at their pre‐clerkship level, to build up more confidence in their scanning ability, improve the orientation of their ultrasound images, and to better understand the relation of the probe’s position to the corresponding images during their clerkship POCUS training. Support or Funding Information Education program anatomy, McMaster University

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)0.008
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.065
GPT teacher head0.339
Teacher spread0.274 · 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 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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Citations0
Published2020
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