MétaCan
Menu
Back to cohort
Record W4295788287 · doi:10.1093/cid/ciac766

Severe Arboviral Neuroinvasive Disease in Patients on Rituximab Therapy: A Review

2022· review· en· W4295788287 on OpenAlexaff
Ronak K. Kapadia, J. Erin Staples, Christine Gill, Marc Fischer, Ezza Khan, Janeen Laven, Amanda J. Panella, Jason O. Velez, Holly R. Hughes, Aaron C. Brault, Daniel M. Pastula, Carolyn V. Gould

Bibliographic record

VenueClinical Infectious Diseases · 2022
Typereview
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of Calgary
FundersNational Institutes of Health
KeywordsRituximabMedicineEncephalitisDiseaseSerologyPediatricsImmunologyVirologyInternal medicineAntibodyVirus

Abstract

fetched live from OpenAlex

With increasing use of rituximab and other B-cell depleting monoclonal antibodies for multiple indications, infectious complications are being recognized. We summarize clinical findings of patients on rituximab with arboviral diseases identified through literature review or consultation with the Centers for Disease Control and Prevention. We identified 21 patients on recent rituximab therapy who were diagnosed with an arboviral disease caused by West Nile, tick-borne encephalitis, eastern equine encephalitis, Cache Valley, Jamestown Canyon, and Powassan viruses. All reported patients had neuroinvasive disease. The diagnosis of arboviral infection required molecular testing in 20 (95%) patients. Median illness duration was 36 days (range, 12 days to 1 year), and 15/19 (79%) patients died from their illness. Patients on rituximab with arboviral disease can have a severe or prolonged course with an absence of serologic response. Patients should be counseled about mosquito and tick bite prevention when receiving rituximab and other B-cell depleting therapies.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.405
Teacher spread0.326 · 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 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

Citations36
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueClinical Infectious DiseasesSame topicMosquito-borne diseases and controlFrench-language works237,207