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Record W2312310502 · doi:10.1097/wco.0000000000000331

Central nervous system inflammation across the age span

2016· review· en· W2312310502 on OpenAlexaff
Amit Bar‐Or, Jack P. Antel

Bibliographic record

VenueCurrent Opinion in Neurology · 2016
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMultiple sclerosisCentral nervous systemDiseaseImmune systemMedicineAge of onsetInflammationImmunologyNeuroimmunologyNeuroscienceDemyelinating diseaseBiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article reviews the influence of age on the development and course of central nervous system (CNS) inflammatory diseases and relates these to genetic and epidemiologic factors and to the age-related properties of both the immune and nervous systems and their interactions. RECENT FINDINGS: There is increased recognition of the onset of multiple sclerosis, the prototype CNS inflammatory/demyelinating disease, outside the expected peak age in young adults. Study of childhood-onset MS cases with comparison to those with uniphasic acquired inflammatory demyelinating syndromes, has helped to better define the clinical spectrum of both types of disorders and provides opportunities to define mechanisms linking recognized genetic and environmental risk factors and disease. Studies of late-onset cases implicate the enhanced role of innate immune mechanisms and immune responses compartmentalized within the CNS in driving a more progressive disease course. The influence of age on incidence and course of multiple sclerosis is compared with the wider spectrum of CNS inflammatory disorders with identified pathogenic mechanisms. SUMMARY: Intrinsic properties of the immune and nervous systems, coupled with the impacts on these systems by environmental factors and by the consequences of CNS tissue injury, underlie the age-related differences in incidence and course of inflammatory/demyelinating disorders of the CNS.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.970
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.179
GPT teacher head0.447
Teacher spread0.268 · 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 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

Citations11
Published2016
Admission routes1
Has abstractyes

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