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Record W2136805706 · doi:10.1001/archneurol.2009.203

Polymyoclonus, Laryngospasm, and Cerebellar Ataxia Associated With Adenocarcinoma and Multiple Neural Cation Channel Autoantibodies

2009· article· en· W2136805706 on OpenAlexaff
Shen‐Yang Lim, Warren Mason, Nathan P. Young, Robert Chen, James H. Bower, Andrew McKeon, Sean J. Pittock, Anthony E. Lang

Bibliographic record

VenueArchives of Neurology · 2009
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Neurological Disorders and Treatments
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsLaryngospasmCerebellar ataxiaAutoantibodyAtaxiaMedicineNeurosciencePsychologyImmunologyAntibodyAnesthesia

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe and provide audiovisual documentation of a syndrome of polymyoclonus, laryngospasm, and cerebellar ataxia associated with adenocarcinoma and multiple neural cation channel autoantibodies. DESIGN: Case report with video. SETTING: University hospitals. Patient A 69-year-old woman presented with subacute onset of whole-body tremulousness and laryngospasm attributed to gastroesophageal reflux. RESULTS: Further evaluation revealed polymyoclonus, cerebellar ataxia, and laryngospasm suspicious of an underlying malignant neoplasm. Surface electromyography of multiple limb muscles confirmed the presence of polymyoclonus. The patient was seropositive for P/Q-type voltage-gated calcium channel antibody; subsequently, whole-body fluorine 18 fluorodeoxyglucose positron emission tomography and cervical lymph node biopsy revealed widespread metastatic adenocarcinoma. Follow-up serologic evaluation revealed calcium channel antibodies (P/Q type and N type) and potassium channel antibody. CONCLUSIONS: We highlight the importance of recognizing polymyoclonus. To our knowledge, this is also the first description of a syndrome of polymyoclonus, laryngospasm, and ataxia associated with adenocarcinoma and these cation channel antibodies.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2009
Admission routes1
Has abstractyes

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