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Record W1904915449 · doi:10.1002/ana.24190

Immunoglobulin <scp>M</scp> oligoclonal bands: Biomarker of targetable inflammation in primary progressive multiple sclerosis

2014· article· en· W1904915449 on OpenAlexaff
Luisa María Villar, Bonaventura Casanova, Nadia Ouamara, Manuel Comabella, Farzaneh Jalili, David Leppert, Clara de Andrés, Guillermo Izquierdo, Rafael Arroyo, Timuçin Avşar, С. В. Лапин, Trina Johnson, Xavier Montalbán, Óscar Fernández, Roberto Álvarez‐Lafuente, Donna Masterman, Maria-Isabel García-Sanchéz, Francisco Coret, Aksel Sıva, Evgeniy Evdoshenko, José C. Álvarez‐Cermeño, Amit Bar‐Or

Bibliographic record

VenueAnnals of Neurology · 2014
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMultiple sclerosisBiomarkerAntibodyInflammationImmunologyMedicineImmunoglobulin GBiologyGenetics

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify a biomarker distinguishing patients who, despite a primary progressive multiple sclerosis (PPMS) clinical course, may nonetheless benefit from immune therapy. METHODS: The presence or absence of both immunoglobulin (Ig) G and IgM oligoclonal bands (OCB) was blindly examined in paired cerebrospinal fluid (CSF) and serum samples from a large PPMS patient cohort, and related to clinical and imaging evidence of focal inflammatory disease activity. RESULTS: Using both cross-sectional samples and serial sampling in a subgroup of patients followed prospectively as part of the placebo-controlled OLYMPUS study of rituximab in PPMS, we found that the presence of CSF-restricted IgM OCB (but not of IgG OCB) is associated with an active inflammatory disease phenotype in PPMS patients. This finding was confirmed in an independent, multicenter validation cohort. INTERPRETATION: The presence of CSF IgM OCB may be a biomarker for a subset of PPMS patients with more active inflammatory disease, who may benefit from immune-directed treatments.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.087
GPT teacher head0.315
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.

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

Citations64
Published2014
Admission routes1
Has abstractyes

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