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Record W3176951054 · doi:10.1016/j.nicl.2021.102734

Multiple sclerosis and subclinical neuropathology in healthy individuals with familial risk: A scoping review of MRI studies

2021· review· en· W3176951054 on OpenAlexaff
Matin Mortazavi, Öznur Hizarci, Lisa Ann Gerdes, Joachim Havla, Tania Kümpfel, Reinhard Hohlfeld, Sophia Stöcklein, Daniel Keeser, Birgit Ertl‐Wagner

Bibliographic record

VenueNeuroImage Clinical · 2021
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersBundesministerium für Bildung und Forschung
KeywordsNeuropathologySubclinical infectionMultiple sclerosisMedicineDiseaseWhite matterEpidemiologyPathologyMagnetic resonance imagingPsychiatryRadiology

Abstract

fetched live from OpenAlex

Multiple genetic and non-heritable factors have been linked to the risk of multiple sclerosis (MS). These factors seem to contribute to disease pathogenesis before the onset of clinical symptoms, as suggested by incidental MRI evidence of subclinical MS neuropathology in individuals without clinical symptoms. Individuals with high familial risk for MS, such as first-degree relatives of patients with MS, can be studied by MRI to characterize the neuropathology during a subclinical period of MS. 16 studies published in English, which performed brain MRI on healthy individuals with high familial risk of MS were included in this scoping review. Studies suggest either no conclusive (5), or inconclusive yet considerable (4), or conclusive evidence (7) for the incidence of subclinical neuropathology, including focal and diffuse tissue damage. Across all studies, white matter lesions fulfilling MS criteria were observed in 86 of 613 individuals (14%). Future research is needed to evaluate the longitudinal dynamics and clinical relevance of preclinical imaging abnormalities in 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)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0150.017
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
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 head0.423
GPT teacher head0.528
Teacher spread0.105 · 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 designSystematic review
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

Citations10
Published2021
Admission routes1
Has abstractyes

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