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Record W2109424918 · doi:10.1017/s0317167100001578

The Role of MRI and Nerve Root Biopsy in the Diagnosis of Neurosarcoidosis

2001· article· en· W2109424918 on OpenAlexaffvenue
Fraser Moore, Frédérick Andermann, John B. Richardson, Donatella Tampieri, Robert Giaccone

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2001
Typearticle
Languageen
FieldMedicine
TopicSarcoidosis and Beryllium Toxicity Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsNeurosarcoidosisMedicineSarcoidosisBiopsyPathologicalNerve rootNerve biopsyRadiologyLumbarCranial nerve palsySurgeryPathologyPeripheral neuropathyDiabetes mellitus

Abstract

fetched live from OpenAlex

OBJECTIVES: Neurological involvement occurs in 5-15% of patients with sarcoidosis and isolated "neurosarcoidosis" occurs in less than 1% of all cases. Classical clinical presentations have been described, such as bilateral facial palsy, but often the disease presents insidiously with varied signs and symptoms. We present a patient who required biopsy of a lumbar nerve root for diagnosis of chronic, progressive neurosarcoidosis and review the literature with an emphasis on diagnosis. METHODS: We have reviewed a patient who presented with signs and symptoms related to infiltration of her meninges and nerve roots by sarcoidosis. All pertinent history and physical information was taken from interviews with the patient and review of her chart. Laboratory, radiographic, and pathological investigations are presented. RESULTS AND CONCLUSIONS: A high index of suspicion is required for the diagnosis of neurosarcoidosis. Gadolinium-enhanced MRI is useful but the findings are often nonspecific, and there should be a low threshold for biopsy whenever the diagnosis is considered.

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.044
GPT teacher head0.304
Teacher spread0.260 · 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 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

Citations21
Published2001
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicSarcoidosis and Beryllium Toxicity ResearchFrench-language works237,207