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Record W4281659487 · doi:10.1177/19418744221104778

Neurology of Acute Viral Infections

2022· review· en· W4281659487 on OpenAlexaff
Jonathan D. Krett, J. David Beckham, Kenneth L. Tyler, Amanda L. Piquet, Lakshmi Chauhan, Carla Wallace, Daniel M. Pastula, Ronak K. Kapadia

Bibliographic record

VenueThe Neurohospitalist · 2022
Typereview
Languageen
FieldMedicine
TopicInfectious Encephalopathies and Encephalitis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineNeurologyIntensive care medicineCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicViral infectionAcute disseminated encephalomyelitis2019-20 coronavirus outbreakVirologyImmunologyDiseaseVirusInfectious disease (medical specialty)PathologyMultiple sclerosisPsychiatryOutbreak

Abstract

fetched live from OpenAlex

As specialists in acute neurology, neurohospitalists are often called upon to diagnose and manage acute viral infections affecting the nervous system. In this broad review covering the neurology of several acute viral infections, our aim is to provide key diagnostic and therapeutic pearls of practical use to the busy neurohospitalist. We will review acute presentations, diagnosis, and treatment of human herpesviruses, arboviruses, enteroviruses, and some vaccine-preventable viruses. The neurological effects of coronaviruses, including COVID-19, are not covered in this review.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.315
Teacher spread0.288 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2022
Admission routes1
Has abstractyes

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