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Development of a Focused Cardiac Ultrasound Image Acquisition Assessment Tool

2020· article· en· W3038650864 on OpenAlexaffabout
Rosemary Adamson, Amy Morris, Jessica Woan, Irene Ma, Daniel Schnobrich, Nilam J. Soni

Bibliographic record

VenueATS Scholar · 2020
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChecklistMedicineDelphi methodSpecialtyMedical educationMedical physicsVotingMEDLINEFamily medicinePsychologyComputer scienceArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

Abstract Background Focused cardiac ultrasound (FCU) is widely used by healthcare providers to answer specific questions about cardiac structure and function at the bedside. Currently, no widely accepted FCU image acquisition checklist exists to assess learners with varying skill levels from different specialties. Objective The primary objective of this project was to develop a consensus-based FCU image acquisition checklist using a multispecialty group of point-of-care ultrasound (POCUS) experts. Methods The essential components of an FCU examination were identified on the basis of published recommendations from echocardiography and international ultrasound societies. A checklist of the essential components of an FCU examination was drafted. A panel of POCUS experts from different medical specialties in the United States and Canada was convened to vote on each checklist item by answering two questions: 1) Is this item important to include in a checklist of essential FCU skills applicable to any medical specialty? and 2) Should the learner be required to successfully complete this item to be considered competent? A modified Delphi approach was used to assess the level of agreement for each checklist item during four rounds of voting. Checklist items that achieved an agreement of 80% or greater were included in the final checklist. Results Thirty-one POCUS experts from seven different medical specialties voted on sixty-five items to be included in the FCU image acquisition assessment tool. The majority of POCUS experts (61%) completed all four rounds of voting. During the first round of voting, 59 items reached consensus, and after revision and revoting, an additional 3 items achieved 80% or greater consensus. A total of 62 items were included in the final checklist, and 57 items reached consensus as a requirement for demonstration of competency. Conclusion We have developed a multispecialty, consensus-based FCU image acquisition checklist that may be used to assess the skills of learners from different specialties. Future steps include studies to develop additional validity evidence for the use of the FCU assessment tool and to evaluate its utility for the translation of skills into clinical practice.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.833
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.349
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations28
Published2020
Admission routes2
Has abstractyes

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