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Record W3037923138 · doi:10.14740/jnr603

The Neurologic Manifestations of Coronavirus Disease 2019

2020· review· en· W3037923138 on OpenAlex
Amjad Elmashala, Saurav Chopra, Aayushi Garg

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Neurology Research · 2020
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePandemicCoronavirusCoronavirus disease 2019 (COVID-19)DiseaseGuillain-Barre syndromeSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Encephalitis2019-20 coronavirus outbreakPneumoniaIntensive care medicinePediatricsVirologyImmunologyInfectious disease (medical specialty)PathologyInternal medicineVirusOutbreak

Abstract

fetched live from OpenAlex

The coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has so far affected 216 countries and more than 5 million individuals worldwide. The infection is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While pulmonary manifestations are the most common, neurological features are increasingly being recognized as common manifestations of the COVID-19, especially in the cases of severe infection. These include acute cerebrovascular disease, encephalitis, and Guillain-Barre syndrome (GBS). Here, we review the neuropathogenesis of SARS-CoV-2 and the central and peripheral nervous system manifestations of COVID-19.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.005
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 head0.209
GPT teacher head0.517
Teacher spread0.308 · 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