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Record W3091747235 · doi:10.3389/fimmu.2020.572186

Revisiting the Pathoetiology of Multiple Sclerosis: Has the Tail Been Wagging the Mouse?

2020· review· en· W3091747235 on OpenAlexaff
Monokesh K. Sen, Mohammed S. M. Almuslehi, Peter Shortland, Jens R. Coorssen, David A. Mahns

Bibliographic record

VenueFrontiers in Immunology · 2020
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsBrock University
FundersWestern Sydney University
KeywordsMultiple sclerosisExperimental autoimmune encephalomyelitisAutoimmunityImmune systemAutoimmune diseaseImmunologyDemyelinating diseaseNeuroscienceDiseaseEncephalomyelitisMedicineBiologyAntibodyPathology

Abstract

fetched live from OpenAlex

Multiple Sclerosis (MS) is traditionally considered an autoimmune-mediated demyelinating disease, the pathoetiology of which is unknown. However, the key question remains whether autoimmunity is the initiator of the disease (outside-in) or the consequence of a slow and as yet uncharacterized cytodegeneration (oligodendrocytosis), which leads to a subsequent immune response (inside-out). Experimental autoimmune encephalomyelitis has been used to model the later stages of MS during which the autoimmune involvement predominates. In contrast, the cuprizone (CPZ) model is used to model early stages of the disease during which oligodendrocytosis and demyelination predominate and are hypothesized to precede subsequent immune involvement in MS. Recent studies combining a boost, or protection, to the immune system with disruption of the blood brain barrier have shown CPZ-induced oligodendrocytosis with a subsequent immune response. In this Perspective, we review these recent advances and discuss the likelihood of an inside-out vs. an outside-in pathoetiology of MS.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.114
GPT teacher head0.319
Teacher spread0.206 · 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

Citations55
Published2020
Admission routes1
Has abstractyes

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