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Record W2060197077 · doi:10.1016/j.nicl.2014.09.003

Magnetization transfer ratio recovery in new lesions decreases during adolescence in pediatric-onset multiple sclerosis patients

2014· article· en· W2060197077 on OpenAlexafffund
Robert A. Brown, Sridar Narayanan, Brenda Banwell, Douglas L. Arnold

Bibliographic record

VenueNeuroImage Clinical · 2014
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMontreal Neurological Institute and Hospital
FundersMultiple Sclerosis SocietyCanadian Institutes of Health ResearchMultiple Sclerosis Scientific Research FoundationBayer HealthCareMultiple Sclerosis Society of CanadaMcGill University
KeywordsRemyelinationMultiple sclerosisMagnetization transferYoung adultMyelinMedicineMagnetic resonance imagingPediatricsPsychologyInternal medicineCentral nervous systemPsychiatryRadiology

Abstract

fetched live from OpenAlex

Children and adolescents diagnosed with multiple sclerosis rarely accrue physical disability early in their disease. This could be explained by greater remyelination in children, a capacity that may be lost in adolescence or early adulthood. Magnetization transfer ratio (MTR) MRI can be used to quantify changes in myelin in MS. We used serial MTR imaging and longitudinal random effects analysis to quantify recovery of MTR in acute lesions and to evaluate MTR changes in normal-appearing tissue in 19 adolescent MS patients. Our objective was to determine whether younger adolescents have a greater capacity for remyelination and whether this decreases as patients approach adulthood. We detected a significant decrease in MTR recovery between ages 16 and 20 years (p = 0.023), with older subjects approaching typical recovery levels for adult-onset MS. MTR recovery in acute MS lesions decreases with age in adolescents, suggesting loss of remyelination capacity. This may be related to the conclusion of primary myelination or other developmental factors.

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)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.105
GPT teacher head0.341
Teacher spread0.235 · 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 designObservational
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

Citations36
Published2014
Admission routes2
Has abstractyes

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