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Record W2121106726 · doi:10.1177/1352458512459684

Oligoclonal bands and cerebrospinal fluid markers in multiple sclerosis: associations with disease course and progression

2012· article· en· W2121106726 on OpenAlexaff
Pedro Lourenço, Afsaneh Shirani, Jameelah Saeedi, Joël Oger, William E. Schreiber, Helen Tremlett

Bibliographic record

VenueMultiple Sclerosis Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMultiple sclerosisCerebrospinal fluidMedicineDiseasePathologyClinically isolated syndromeImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: The use of oligoclonal bands (OCBs) and cerebrospinal fluid (CSF) parameters are established in the diagnosis of MS, but poorly as markers of disease. OBJECTIVE: To investigate the role of OCBs in disease course and progression. METHODS: CSF data for 1120 patients with MS were analyzed for associations between OCBs and CSF parameters and clinical data (disease course [relapsing-onset MS (ROMS) vs primary-progressive MS (PPMS)]), disability progression (proportion reaching Expanded Disability Status Scale 6 within 10 years of onset and progression index) and ethnicity. RESULTS: Of patients with MS, 72.5% had detectable OCBs. For patients with detectable OCBs, 84.6% had ROMS and 15.4% PPMS versus 89.7% and 10.3%, respectively for those without detectable OCBs (p=0.04). Total CSF IgG and protein levels were higher in PPMS compared with ROMS (p<0.001). Disease progression appeared independent of OCB status. Patients with CSF (vs without) data were more likely to be male, older at onset, have PPMS and lack optic neuropathy at onset (p<0.001). CONCLUSIONS: OCB positivity and elevated total CSF IgG and protein were moderately associated with a PPMS disease course, but not disease progression. Patients with atypical clinical presentations were more likely to have had CSF work-up, suggesting a testing bias.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.082
GPT teacher head0.310
Teacher spread0.228 · 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.

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

Citations55
Published2012
Admission routes1
Has abstractyes

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