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Record W4234823605 · doi:10.1017/cjn.2019.117

P.016 Clinical course of relapsing remitting multiple sclerosis post-natalizumab

2019· article· en· W4234823605 on OpenAlexvenueaboutno aff
MD Fiander, V Bhan, SA Stewart, NE Parks

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2019
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsnot available
Fundersnot available
KeywordsNatalizumabFingolimodMedicineMultiple sclerosisProgressive multifocal leukoencephalopathyInternal medicineRelapsing remittingConfidence intervalGastroenterologyImmunology

Abstract

fetched live from OpenAlex

Background: Natalizumab is an efficacious disease modifying therapy (DMT) for relapsing remitting multiple sclerosis (RRMS), however, duration of therapy is often limited by risk of progressive multifocal leukoencephalopathy (PML). We describe the clinical course of RRMS patients switched from natalizumab to another DMT in a Canadian MS clinic. Methods: We conducted a retrospective study of prospectively collected data from the Dalhousie Multiple Sclerosis Research Unit (DMSRU). We identified all RRMS patients treated with natalizumab for >3 months who discontinued therapy with serum JC virus antibody positive status and switched to another DMT. Results: There were 84 individuals who switched to another DMT following natalizumab with 57 (68%) switching to fingolimod. Survival without a relapse on fingolimod was 92% (95% confidence interval 80-97%) at 6 months, 90% (77-96%) at 12 months, 85% (71-93%) at 24 months, 74% (56-86%) at 36 months. Survival without disease progression on fingolimod was 90% (95% CI 78-96%) at 6 months, 86% (72-93%) at 12 months, 78% (63-88%) at 24 months, 78% (63-88%) at 36 months. Conclusions: Although alternative DMTs may be used post-natalizumab, fingolimod remains an effective therapy with a high proportion of patients remaining free of relapses or progression at 3 years.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.062
GPT teacher head0.311
Teacher spread0.249 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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