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Record W3112161524 · doi:10.1016/j.msard.2020.102673

Effect of Ozanimod on Symbol Digit Modalities Test Performance in Relapsing MS

2020· article· en· W3112161524 on OpenAlexafffund
John DeLuca, Sven Schippling, Xavier Montalbán, Ludwig Kappos, Bruce Cree, Douglas L. Arnold, Hans‐Peter Hartung, James K. Sheffield, Hongjuan Liu, Diego Augusto Santos Silva, Jeffrey A. Cohen

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityNeuroRx Research (Canada)Montreal Neurological Institute and Hospital
FundersCelgeneBristol-Myers Squibb CanadaBristol-Myers Squibb
KeywordsMedicineNumerical digitTest (biology)Symbol (formal)ModalitiesMultiple sclerosisAudiologyDermatologyArithmeticPsychiatryMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Cognitive dysfunction, including slowed cognitive processing speed (CPS), is one of the most disabling symptoms of multiple sclerosis (MS). The Symbol Digit Modalities Test (SDMT) is a preferred measure of CPS for MS trials and routine screening. Based on encouraging SDMT results in the phase 3 SUNBEAM trial, these post hoc, exploratory analyses were conducted to further compare effects of the sphingosine 1-phosphate receptor modulator ozanimod versus intramuscular interferon β-1a on CPS in participants with relapsing multiple sclerosis (RMS). METHODS: In the phase 3, double-blind, double-dummy, SUNBEAM study, adults (aged 18‒55 years) with RMS (N=1,346) were randomized to once-daily oral ozanimod 0.92 or 0.46 mg, or weekly intramuscular interferon β-1a 30 µg. The study continued until the last participant was treated for 12 months. CPS was measured as part of a secondary endpoint using the SDMT. Exploratory, post hoc analyses evaluated SDMT change and percentages of participants with clinically meaningful (≥4-point) SDMT improvement or worsening at months 6 and 12, and relationship between SDMT and brain volume on magnetic resonance imaging. RESULTS: Ozanimod improved SDMT scores compared with interferon β-1a at months 6 and 12. At month 12, least squares mean difference in SDMT z-scores for ozanimod 0.92 mg versus interferon β-1a was 0.102 (95% CI, 0.031‒0.174, nominal p = 0.0051; standardized mean difference = 0.1376). A greater percentage of ozanimod 0.92 mg‒treated participants had clinically meaningful improvements in SDMT scores versus interferon β-1a at month 6 (30.0% versus 22.2%) and month 12 (35.6% versus 27.9%). Of those with SDMT improvement at month 6, 66.4% of those treated with ozanimod 0.92 mg and 55.9% of those treated with interferon β-1a had sustained improvement at month 12. Brain volume loss was similar for those with SDMT improvement versus worsening at month 12. CONCLUSIONS: In these exploratory analyses, ozanimod had modestly beneficial effects on CPS in RMS participants. The effects of ozanimod on SDMT are being further evaluated in an ongoing 3-year clinical trial. SUNBEAM is registered on clinicaltrials.gov (NCT02294058) and the European Clinical Trials Database (EudraCT 2014-002320-27).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.256
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations33
Published2020
Admission routes2
Has abstractno

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