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Record W2415008095 · doi:10.7759/cureus.636

Expert Facilitated Development of an Objective Assessment Tool for Point-of-Care Ultrasound Performance in Undergraduate Medical Education

2016· article· en· W2415008095 on OpenAlexaff
Holly A. Black, Gillian Sheppard, Brian L. Metcalfe, Jordan Stone‐McLean, Heather McCarthy, Adam Dubrowski

Bibliographic record

VenueCureus · 2016
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicinePoint of care ultrasoundPoint-of-care testingMedical educationPoint of careMedical physicsUltrasoundNursingPathologyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: With the various applications of point-of-care ultrasound (PoCUS) steadily increasing, many medical schools across North America are incorporating PoCUS training into their undergraduate curricula. The Faculty of Medicine at Memorial University also intends to introduce PoCUS training into its own undergraduate medical program. The proposed approach is to introduce a PoCUS curriculum focusing on anatomy and physiology while developing cognitive and psychomotor skills that are later transferred into clinical applications. This has been the common approach taken by most undergraduate ultrasound programs in the United States. This project highlights the development and the challenges involved in creating an objective assessment tool that meets the unique needs of this proposed undergraduate ultrasound curriculum. METHODS: After a thorough review of existing literature and input from experts in PoCUS, a prototype global rating scale (GRS) and three exam-specific checklists were created by researchers. The exam-specific checklists include aorta exam, subxiphoid cardiac exam, and focused abdominal exam. A panel of 18 emergency room physicians certified in PoCUS were recruited to evaluate the GRS and three checklists. This was accomplished using a modified Delphi technique. The items were rated on a 5-point Likert scale. If an item received a mean score of less than 4, it was deemed unimportant for the assessment of PoCUS performance in undergraduate medical learners and was excluded. Experts were also encouraged to provide comments and suggest further items to be added to the GRS or checklists. Items were modified according to these comments. All of the edits were then sent back to the experts for revisions. RESULTS: A consensus was achieved after three rounds of surveys, with the final GRS containing nine items. The final aorta checklist contained nine items, and the subxiphoid cardiac and focused abdominal checklists each contained 11 items. CONCLUSION: By using a modified Delphi technique, we developed a single GRS and three checklists. A panel of independent PoCUS practitioners supports the content validity of these tools. Research is currently ongoing to evaluate their validity for assessing PoCUS competency in undergraduate medical students.

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)0.063
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.063
Threshold uncertainty score0.335

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.104
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.027
GPT teacher head0.382
Teacher spread0.354 · 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 designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations24
Published2016
Admission routes1
Has abstractyes

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