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Record W4220846499 · doi:10.1016/j.clicom.2022.03.004

Meningoradiculitis post-COVID-19 mRNA vaccination: A case report

2022· article· en· W4220846499 on OpenAlexaff
Alexandre Landry, Stéphanie Crapoulet, Luc H. Boudreau, Christine Bourque, Lyle Weston, Nicholas Pilote, Guillaume Desnoyers, Ludivine Chamard-Witkowski

Bibliographic record

VenueClinical Immunology Communications · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMoncton HospitalVitalité Health NetworkHorizon Health NetworkUniversité de MonctonDr. Georges-L.-Dumont University Hospital Centre
Fundersnot available
KeywordsVaccinationMedicineContext (archaeology)PediatricsImmunology

Abstract

fetched live from OpenAlex

We present a rare case of meningoradiculitis occurring after mRNA COVID-19 vaccination. This patient, with a history of inflammatory arthritis following rubella vaccination, presented to the emergency department 4 days after her vaccination with both central and radicular nervous system symptoms. Symptoms included pain, sensory and motor deficits in L5 roots distribution, along with signs of central irritation, such as headache, difficulty concentrating and a Babinski sign. MRI showed bilateral L5 nerve roots enhancement. Lumbar puncture showed elevated protein and IgG, and relevant serologies excluded common causes. Prednisone and physical therapy helped the patient to achieve near complete recovery nine weeks after presentation. We concluded that this patient presented meningoradiculitis probably secondary to her vaccination in a context of possible overactive immune system. While such presentations might be rare, and do not constitute a general reason to abstain from vaccination, they must be well recognized and treated.

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)0.003
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 score0.007
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.152
GPT teacher head0.477
Teacher spread0.325 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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