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Record W4200402610 · doi:10.1017/cjn.2021.508

Complement Inhibition in Myasthenia Gravis and Neuromyelitis Optica Spectrum Disorder

2021· review· en· W4200402610 on OpenAlexaffvenueabout
Dubravka Dodig, Angela Genge, Daniel Selchen, Mark S. Freedman

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2021
Typereview
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsSt. Michael's HospitalUniversity of OttawaUniversity of TorontoMontreal Neurological Institute and HospitalOttawa HospitalToronto Western Hospital
FundersEMD SeronoBayer HealthCareAtara BiotherapeuticsAlexion PharmaceuticalsTeva Pharmaceutical IndustriesCelgeneSanofi
KeywordsNeuromyelitis opticaMyasthenia gravisPathogenesisComplement systemImmunologySpectrum disorderComplement (music)MedicineClassical complement pathwayInnate immune systemAlternative complement pathwayNeuroscienceImmune systemAntibodyBiologyGeneticsPsychiatry

Abstract

fetched live from OpenAlex

The complement system is a tightly controlled signaling network that plays a role in innate immune surveillance. However, abnormal signaling through this pathway contributes to tissue damage in several inflammatory, autoimmune, and degenerative diseases. Myasthenia gravis (MG) and neuromyelitis optica spectrum disorders (NMOSD) have complement dysfunction at the core of pathogenesis, providing a strong rationale for therapeutic targeting of complement components. The purpose of this paper is to briefly review the role of complement activation in the pathogenesis of MG and NMOSD, to discuss the rationale and evidence for complement inhibition as a method to manage these diseases, and to provide a Canadian perspective on the use of complement inhibition therapy through real-world cases of MG and NMOSD.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.316
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicMyasthenia Gravis and ThymomaFrench-language works237,207