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Record W4411711915 · doi:10.1016/j.neurot.2025.e00631

Pediatric multiple sclerosis: Improving outcome through high-efficacy therapies

2025· review· en· W4411711915 on OpenAlexafffund
Lama Aljomah, E. Ann Yeh

Bibliographic record

VenueNeurotherapeutics · 2025
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsSickKids Foundation
FundersNational Institutes of HealthCanadian Institutes of Health ResearchEpilepsy SocietySick Kids FoundationHospital for Sick ChildrenAlexion PharmaceuticalsGarry Hurvitz Centre for Brain and Mental HealthOntario Institute for Regenerative MedicineCanadian Society of NephrologyRare Disease FoundationUniversity of TorontoBiogenNational Multiple Sclerosis Society
KeywordsMultiple sclerosisNeurologyMedicineNeurosurgerySurgeryPsychiatry

Abstract

fetched live from OpenAlex

Pediatric-onset multiple sclerosis (POMS) refers to multiple sclerosis occurring in individuals under 18 years of age. It is characterized by poor cognitive outcomes and a more inflammatory course, more frequent clinical relapses, and a greater number of MRI lesions compared to adult-onset MS (AOMS). Prompt recognition of multiple sclerosis in this population is essential, as early intervention with disease-modifying therapies may change the trajectory of disease progression. In this paper, we will review diagnostic criteria for pediatric multiple sclerosis, differential diagnosis, and current and emerging therapeutic approaches. While a number of DMTs are approved for adult MS, few are approved for pediatric use. Many of these DMTs are used off-label, with real-world evidence demonstrating their effectiveness and safety. The review evaluates existing evidence for the use of these therapies in pediatric populations, with an emphasis on both existing clinical trials and real-world data that supports their use. In addition, we will briefly highlight ongoing clinical trials and emerging therapies for POMS.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.255
GPT teacher head0.407
Teacher spread0.152 · 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.

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

Citations4
Published2025
Admission routes2
Has abstractyes

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