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Record W4396804149 · doi:10.26565/2313-6693-2024-48-03

Clinical features of cognitive dysfunction in patients with relapsing-remitting type of multiple sclerosis

2024· article· en· W4396804149 on OpenAlexaboutno aff
Олександра Тесленко, Olena Tovazhnyanska

Bibliographic record

VenueThe Journal of V N Karazin Kharkiv National University series Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRelapsing remittingMultiple sclerosisCognitionMedicineImmunologyPsychiatry

Abstract

fetched live from OpenAlex

Background. Cognitive dysfunction in patients with multiple sclerosis is quite common, but attention is not always paid to it, since the decline of cognitive functions is often masked by motor, sensory, and visual disorders. Active patient questioning and neurocognitive screening are needed to identify cognitive impairment in patients with multiple sclerosis, even in the early stages of the disease. The goal of the study is to determine the frequency, severity, and clinical features of cognitive impairment in patients with relapsing-remitting multiple sclerosis, taking into account the duration of the disease and the level of disability of the patients. Materials and Methods. 67 patients with a diagnosis of relapsing-remitting multiple sclerosis were examined. All examined patients underwent a thorough neurological, psychometric, and instrumental examination. Patients were divided into 3 groups depending on the duration of the disease: 1st group up to 5 years (24 patients), 2nd group – from 5 to 10 years (22 patients), 3rd group more than 10 years (21 patients). The Symbol Digit Modalities Test (SDMT) and the Montreal Cognitive Function Assessment Scale (MoCA) were used to assess patients’ neuropsychological status. Results. The conducted correlation analysis showed the presence of a probable inverse relationship between the score on the EDSS scale and the scores on the SDMT and MoSA scales (r = –0.61 (p0.05); r = –0.12 (p>0.05) for scores on SDMT and MoCA scales, respectively). We also obtained a probable directly proportional correlation between the test scores on the MoСA scale and SDMT (in 1st group = 0.63, p<0.05, in 2nd group = 0.89, p<0.05, in the 3rd group r = 0.64, p<0.05) in all studied groups, i.e. for all periods of the disease duration. Conclusions. The obtained data of the correlation analysis indicate a relationship between the severity of cognitive impairment according to the test scores, the degree of disability of the patients, and the duration of the disease.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.319
Teacher spread0.251 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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