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Record W3167501016 · doi:10.15173/mumj.v18i1.2590

rare presentation of autoimmune limbic encephalitis with anti-Yo antibodies: Case report

2021· article· en· W3167501016 on OpenAlexaff
Eva Liu, Janhavi Patel, Alicia Mattia, Han‐Oh Chung

Bibliographic record

VenueMcMaster University Medical Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Neurological Disorders and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLimbic encephalitisPresentation (obstetrics)MedicineAntibodyImmunologyAutoimmune encephalitisEncephalitisVirologyAutoantibodyVirusSurgery

Abstract

fetched live from OpenAlex

Introduction: Paraneoplastic limbic encephalitis is a non-metastatic complication of malignant disease characterized by subacute neuropsychiatric symptoms and short-term memory deficits. Case: We present an atypical case of a 38-year-old, previously healthy female with recurrent seizures, severe persistent short-term memory loss, and emotional lability. The patient was diagnosed with autoimmune limbic encephalitis confirmed by magnetic resonance imaging findings and positive anti-Yo antibodies. She screened negative for occult malignancies. The patient responded to daily prednisone and intravenous immunoglobulins and her cognitive deficits were resolved. Conclusion: This is an unusual case of autoimmune encephalitis as anti-Yo antibodies are typically associated with cerebellar dysfunction. Our patient’s case adds to the one other published case showing induction of limbic encephalitis due to anti-Yo antibodies, and prompts consideration of paraneoplastic anti-Yo limbic encephalitis as a rare cause of symptoms in patients with limbic encephalitis-like symptoms and no known etiology.

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 score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.256
Teacher spread0.239 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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