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Record W3024652151 · doi:10.1017/cjn.2020.96

The Virtual Neurologic Exam: Instructional Videos and Guidance for the COVID-19 Era

2020· review· en· W3024652151 on OpenAlex
Mariam Al Hussona, Monica Maher, David Chan, Jonathan A. Micieli, Jennifer D. Jain, Houman Khosravani, Aaron Izenberg, Charles D. Kassardjian, Sara Mitchell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2020
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)TelemedicinePandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirtual patientMedicineCognitionNeurological examinationVariety (cybernetics)Physical examinationPsychologyComputer sciencePhysical medicine and rehabilitationMedical physicsMedical educationHealth carePsychiatryArtificial intelligencePathologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To outline features of the neurologic examination that can be performed virtually through telemedicine platforms (the virtual neurological examination [VNE]), and provide guidance for rapidly pivoting in-person clinical assessments to virtual visits during the COVID-19 pandemic and beyond. METHODS: The full neurologic examination is described with attention to components that can be performed virtually. RESULTS: A screening VNE is outlined that can be performed on a wide variety of patients, along with detailed descriptions of virtual examination maneuvers for specific scenarios (cognitive testing, neuromuscular and movement disorder examinations). CONCLUSIONS: During the COVID-19 pandemic, rapid adoption of virtual medicine will be critical to provide ongoing and timely neurological care. Familiarity and mastery of a VNE will be critical for neurologists, and this article outlines a practical approach to implementation.

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.

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)0.009
metaresearch head score (Gemma)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0090.018
Scholarly communication0.0020.001
Open science0.0040.000
Research integrity0.0010.004
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 head0.069
GPT teacher head0.346
Teacher spread0.277 · 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