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Record W4387210069 · doi:10.1177/13524585231201219

Detecting isolated cognitive relapses in persons with MS

2023· article· en· W4387210069 on OpenAlexaff
Sarah A. Morrow, Zachary Weinstock, Omid Mirmosayyeb, Devon Conway, Tom Fuchs, Michael Jaworski, Sveltlana Eckert, David Hojnacki, Michael G. Dwyer, Robert Zivadinov, Bianca Weinstock‐Guttman, Ralph H. B. Benedict

Bibliographic record

VenueMultiple Sclerosis Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsLondon Health Sciences CentreWestern University
FundersNational Multiple Sclerosis Society
KeywordsLogistic regressionMultiple sclerosisConfidence intervalNeuropsychologyProspective cohort studyCohortOdds ratioPsychologyMedicineCognitive declineDemographicsExpanded Disability Status ScaleDemographyCognitionInternal medicineDiseasePsychiatryDementia

Abstract

fetched live from OpenAlex

BACKGROUND: The existence of isolated cognitive relapses (ICRs) in persons with MS (PwMS) has been debated. OBJECTIVE: To examine relapses with decline on Symbol Digit Modalities Test (SDMT) but no change on Expanded Disability Status Scale (EDSS). METHODS: This 3-year prospective cohort study identified PwMS experiencing a relapse with decrease on SDMT. Participants with SDMT decline/stable EDSS were labeled "ICR," while those with a corresponding decrease on EDSS were classified "Relapse with Cognitive Decline (RCD)." Two definitions of SDMT decline were explored: (1) ⩾ 8 points, and (2) ⩾ 4 points. Logistic regression was used to analyze the relationship between ICR and RCD. RESULTS: = 0.024) using demographics and disease characteristics explained 28.4% of the variance in ICR versus RCD. Only the MS Neuropsychological Questionnaire was associated with ICR (odds ratio (OR): 8.6; 95% confidence interval (CI): 1.1-16.4) 40 relapsing participants with SDMT decrease of ⩾ 4 points were identified: 26 (65%) had a stable EDSS (ICR). Logistic regression did not find any variable predictive of ICR. CONCLUSION: This prospective study demonstrates evidence of ICR in PwMS.

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)0.003
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 score0.082
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.114
GPT teacher head0.317
Teacher spread0.202 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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