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Record W2886409367 · doi:10.1017/cjn.2018.28

JC Virus Granule Cell Neuronopathy as AIDS-Presenting Illness

2018· article· en· W2886409367 on OpenAlexaffvenue
Simon Grandjean Lapierre, Xin Dang, Danielle Gilbert, Sylvie Lauzier, Igor J. Koralnik, Louiselle LeBlanc

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2018
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsHôpital du Sacré-Cœur de MontréalUniversité de MontréalCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Neurological Disorders and StrokeUniversity of Cambridge
KeywordsProgressive multifocal leukoencephalopathyJC virusLeukoencephalopathyMedicineVirusDemyelinating diseaseSlow virusEtiologyEncephalitisImmunologyOpportunistic infectionVirologyPathologyDiseaseViral disease

Abstract

fetched live from OpenAlex

JC virus is the etiological agent of progressive multifocal leukoencephalopathy, a white matter demyelinating disease that mostly affects immunocompromised patients. JC virus can also infect neurons and meningeal cells and cause encephalitis, meningitis and granule cell neuronopathy. We report a patient with JC virus granule cell neuronopathy, without concomitant progressive multifocal leukoencephalopathy, presenting as inaugural acquired immune deficiency syndrome-related illness. This patient's human immunodeficiency virus infection remained undiagnosed for several months after neurological symptoms onset. We review JC virus pathophysiology, clinical manifestations, treatment and prognosis, and emphasize the importance of considering human immunodeficiency virus infection and related opportunistic infections in the differential diagnosis of new-onset isolated cerebellar disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations3
Published2018
Admission routes2
Has abstractyes

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