MétaCan
Menu
Back to cohort
Record W2801723726 · doi:10.1177/1352458518770085

A 24-month advanced magnetic resonance imaging study of multiple sclerosis patients treated with alemtuzumab

2018· article· en· W2801723726 on OpenAlexaff
Irene M. Vavasour, Roger Tam, David Li, Cornelia Laule, Carolyn Taylor, Shannon Kolind, Alex L. MacKay, Adil Javed, Anthony Traboulsee

Bibliographic record

VenueMultiple Sclerosis Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
FundersSanofi GenzymeDartmouth College
KeywordsMultiple sclerosisAlemtuzumabWhite matterMedicineCorpus callosumMagnetic resonance imagingFractional anisotropyMyelinDiffusion MRIPathologyNuclear medicineRadiologyCentral nervous systemInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Tissue damage in both multiple sclerosis (MS) lesions and normal-appearing white matter (NAWM) are important contributors to disability and progression. Specific aspects of MS pathology can be measured using advanced imaging. Alemtuzumab is a humanised monoclonal antibody targeting CD52 developed for MS treatment. OBJECTIVE: To investigate changes over 2 years of advanced magnetic resonance (MR) metrics in lesions and NAWM of MS patients treated with alemtuzumab. METHODS: and myelin water fraction (MWF) were determined for stable lesions, new lesions and NAWM. Fractional anisotropy was calculated for the corpus callosum (CC) and N-acetylaspartate (NAA) concentration was determined from a large NAWM voxel. Brain parenchymal fraction (BPF), cortical thickness and CC area were also calculated. RESULTS: No change in any MR measurement was found in lesions or NAWM over 24 months. BPF, cortical thickness and CC area all showed decreases in the first year followed by stability in the second year. CONCLUSION: Advanced MR biomarkers of myelin (MWF) and neuron/axons (NAA) show no change in NAWM over 24 months in alemtuzumab-treated MS participants.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.061
GPT teacher head0.281
Teacher spread0.220 · 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

Citations24
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMultiple Sclerosis JournalSame topicMultiple Sclerosis Research StudiesFrench-language works237,207