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Record W4415224678 · doi:10.1016/j.msard.2025.106799

The influence of manual dexterity on processing speed assessment in multiple sclerosis: A comparison of the PST and oral SDMT

2025· article· en· W4415224678 on OpenAlexaff
Sarah A. Morrow, Marina R. Everest, David Beniameen, Heather Rosehart

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsLondon Health Sciences CentreWestern UniversityUniversity of Calgary
FundersBiogen IdecBiogen
KeywordsMultiple sclerosisTest (biology)CohortExpanded Disability Status ScaleCognitionCorrelation

Abstract

fetched live from OpenAlex

Cognitive impairment (CI) is a common symptom in persons with Multiple Sclerosis (PwMS), specifically information processing speed (IPS) impairment. The Symbol Digit Modalities Test (SDMT), which requires in-person testing, is a well-established measure for screening for IPS and CI in clinic settings. The Processing Speed Test (PST), a self-administered iPad-based tool, has been proposed as an alternative. Preliminary studies suggest that the PST can effectively differentiate PwMS from healthy controls and correlates with MRI results. This study aims to investigate if the relationship between the PST and oral SDMT is affected by manual dexterity or level of disability. A cohort of 100 PwMS completed both the PST and SDMT, as well the 9-Hole Peg Test (9HPT) and Expanded Disability Status Scale (EDSS). The strong positive correlation found between the PST and SDMT was maintained across all subgroups based on 9HPT performance and EDSS functional system scores. Our findings indicate that the PST is comparable to the SDMT for PwMS regardless of manual dexterity. This validates PST as a time-efficient, self-administered alternative that can reduce clinical burden in MS care.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.050
GPT teacher head0.333
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes1
Has abstractyes

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