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Record W3082363863 · doi:10.3389/fneur.2020.00784

Multiple Sclerosis as a Syndrome—Implications for Future Management

2020· article· en· W3082363863 on OpenAlexaff
Chris Dwyer, Linda Nguyen, Luke M. Healy, Ranjan Dutta, Samuel K. Ludwin, Jack P. Antel, Michele D. Binder, Trevor J. Kilpatrick

Bibliographic record

VenueFrontiers in Neurology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersMultiple Sclerosis AustraliaNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Health and Medical Research CouncilState Government of VictoriaAustralian GovernmentStrong
KeywordsMultiple sclerosisNeuroscienceMERTKPhenocopyDiseaseImmune systemBiologyComputational biologyMedicineImmunologyPhenotypeGeneticsGenePathologyReceptor

Abstract

fetched live from OpenAlex

We propose that multiple sclerosis (MS) is best characterized as a syndrome rather than a single disease because different pathogenetic mechanisms can result in the constellation of symptoms and signs by which MS is clinically characterized. We describe several cellular mechanisms that could generate inflammatory demyelination through disruption of homeostatic interactions between immune and neural cells. We illustrate that genomics is important in identifying phenocopies, in particular for primary progressive MS. We posit that molecular profiling, rather than traditional clinical phenotyping, will facilitate meaningful patient stratification, as illustrated by interactions between HLA and a regulator of homeostatic phagocytosis, MERTK. We envisage a personalized approach to MS management where genetic, molecular, and cellular information guides management.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.005
Open science0.0020.002
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.220
Teacher spread0.196 · 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 designTheoretical or conceptual
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

Citations5
Published2020
Admission routes1
Has abstractyes

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