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Record W3111549254 · doi:10.1016/j.arrct.2020.100098

Education in the Time of COVID: At-a-Distance Training in Neuromusculoskeletal Ultrasonography

2020· article· en· W3111549254 on OpenAlexaffabout
Amy Cook, Peter Inkpen

Bibliographic record

VenueArchives of Rehabilitation Research and Clinical Translation · 2020
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsSpecialtyCurriculumMedical educationCoronavirus disease 2019 (COVID-19)MedicinePoint of care ultrasoundUltrasonographySet (abstract data type)Variety (cybernetics)Point (geometry)UltrasoundFamily medicinePsychologyDiseaseRadiologyPathologyPedagogyComputer scienceInfectious disease (medical specialty)Artificial intelligence

Abstract

fetched live from OpenAlex

Point of care ultrasound is important to the specialty of physical medicine and rehabilitation (PM&R) to aid in the diagnosis and treatment of a variety of neuromusculoskeletal conditions commonly seen in practice. However, across Canada, resident education of sonoanatomy skills is variable. There remain no standards in terms of how ultrasound is taught as part of the residency curriculum as set by the Royal College of Physicians and Surgeons of Canada. As such, residents are often required to find their own educational opportunities. This report describes an alternative approach to learning these skills that was inspired by disruption due to coronavirus disease 2019 in first year residency. This report explores how a PM&R resident was able to develop valuable ultrasound skills from home using not only textbooks and videos, but also new and novel teleguidance technology, namely an ultrasound probe that connects to a clinician's own smart devices to display images.

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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.154
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.108
GPT teacher head0.453
Teacher spread0.345 · 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 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".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

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