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Record W4360999432 · doi:10.1016/j.msard.2023.104677

Longitudinal assessment of neurocognitive function in people with relapsing multiple sclerosis initiating alemtuzumab in routine clinical practice: LEM-COG study results

2023· article· en· W4360999432 on OpenAlexaffabout
Jeffrey Wilken, Anthony Traboulsee, Flavia Nelson, Carolina Ionete, Shannon Kolind, Timothy Fratto, Robert L Kane, Roopali Gandhi, Andreea M. Rawlings, Nora Roesch, Mark A. Ozog, John DeLuca

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPaced Auditory Serial Addition TestMedicineNeurocognitiveDepression (economics)Confidence intervalPopulationMallinckrodtExpanded Disability Status ScalePhysical therapyCognitionNeuropsychologyPhysical medicine and rehabilitationPsychiatryInternal medicineMultiple sclerosis

Abstract

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BACKGROUND: Alemtuzumab is effective in reducing relapse rate and disability, but limited data exist on its effect on cognitive function in relapsing multiple sclerosis (RMS). The present study assessed neurocognitive function and safety associated with alemtuzumab treatment in RMS. METHODS: This longitudinal, single-arm, prospective study included people with RMS (aged 25-55 years) who were treated with alemtuzumab in clinical practice in the United States of America and Canada. The first participant was enrolled in December 2016. The primary endpoint was the change from baseline to post-baseline (month [M] 12/24) in MS-COGnitive (MS-COG) composite score. Secondary endpoints included Paced Auditory Serial Addition Test (PASAT), Symbol Digit Modalities Test (SDMT), Brief Visuospatial Memory Test-Revised (BVMT-R), Selective Reminding Test (SRT), Controlled Oral Word Association Test (COWAT), and Automated Neuropsychological Assessment Metrics (ANAM) scores. Depression and fatigue were assessed using Hamilton Rating Scale-Depression (HAM-D) and Fatigue Severity Scale (FSS)/Modified Fatigue Impact Scale (MFIS), respectively. Magnetic resonance imaging (MRI) parameters were assessed when available. Safety was assessed throughout the study. Descriptive statistics were used for the pre-specified statistical analyses. Since the study was terminated early (November 2019) because of operational and resource difficulties, post hoc analyses for statistical inference were performed among participants who had a baseline value and at least one complete post-baseline assessment for cognitive parameters, fatigue, or depression. RESULTS: Of the 112 participants enrolled, 39 were considered as the primary analysis population at M12. At M12, a mean change of 0.25 (95% confidence interval [CI]: 0.04, 0.45; p = 0.0049; effect size [ES]: 0.39) was observed in the MS-COG composite score. Improvements were observed in processing speed (based on PASAT and SDMT; p < 0.0001; ES: 0.62), as well as in individual PASAT, SDMT and COWAT scores. An improvement was also noted in HAM-D (p = 0.0054; ES: -0.44), but not in fatigue scores. Among MRI parameters, decreases in burden of disease volume (BDV; ES: -0.12), new gadolinium-enhancing lesions (ES: -0.41) and newly active lesions (ES: -0.07) were observed at M12. About 92% of participants showed stable or improved cognitive status at M12. There were no new safety signals reported in the study. The most common adverse events (≥10% of participants) were headache, fatigue, nausea, insomnia, urinary tract infection, pain in extremity, chest discomfort, anxiety, dizziness, arthralgia, flushing, and rash. Hypothyroidism (3.7%) was the most frequent adverse event of special interest. CONCLUSION: The findings from this study suggest that alemtuzumab has a positive impact on cognitive function with significant improvements in processing speed and depression in people with RMS over a period of 12 months. The safety profile of alemtuzumab was consistent with previous studies.

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)0.004
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.099
GPT teacher head0.366
Teacher spread0.267 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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