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Record W4205747186 · doi:10.1016/j.msard.2021.103479

Comparing diagnostic criteria for the diagnosis of neurocognitive disorders in multiple sclerosis

2022· article· en· W4205747186 on OpenAlexfundno aff
Laura M. Hancock, Bruce P. Hermann, Menno M. Schoonheim, Scott Hetzel, Bruno Brochet, John DeLuca

Bibliographic record

VenueMultiple Sclerosis and Related Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersEMD SeronoEurostarsAmsterdam NeuroscienceZonMwMultiple Sclerosis Society of CanadaNational Institutes of HealthFondation pour l'Aide à la Recherche sur la Sclérose en PlaquesAtara BiotherapeuticsCelgeneBiogenNational Multiple Sclerosis SocietyMedDay PharmaceuticalsSanofi
KeywordsMcNemar's testMedicineNeurocognitiveCognitionMultiple sclerosisCognitive impairmentClinical psychologyNormativeCognitive testPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: People with multiple sclerosis (MS) commonly experience cognitive impairment associated with the disease, but there is currently no agreed-upon operational definition for identifying the presence of that impairment, in either research or clinic contexts. The International MS Cognition Society (IMSCOGS) established a task force to begin to examine this issue and this paper represents the results of an initial pilot investigation. The aim of this paper was to compare two criterion sets to determine how to identify cognitive impairment among people with MS: the general Diagnostic and Statistical Manual (DSM-5) Criteria for neurocognitive disorders and criteria derived from existing MS research (scores in two domains fall 1.5 standard deviations below normative controls). METHODS: Two hundred and ten people with MS presented for a brief cognitive evaluation in an MS Multidisciplinary Clinic at a midwestern academic medical center in the United States. Participants were generally middle aged (average 51.5 years), female (73.8%), and white (93.3%). McNemar's test was computed to compare the number of individuals whose cognitive test score performance was deemed cognitively normal, mildly impaired, or more significantly impaired. RESULTS: DSM-5 criteria classified 87.2% of the sample as cognitively impaired, where 66.7% were more mildly impaired and 20.5% more significantly impaired. By contrast, research-based criteria classified 63.3% of the sample as cognitively impaired, with 49.5% as mildly impaired and 13.8% as more significantly impaired. CONCLUSIONS: These findings indicate that compared to research criteria, the DSM-5 criteria classified far more people with MS as having cognitive impairment secondary to the disease. The paper discusses the potential benefits and drawbacks of the two diagnostic methods, highlighting that more work will be needed in order to establish a standardized and validated method for characterizing these impairments.

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)0.010
metaresearch head score (Gemma)0.046
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 score0.013
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.090
GPT teacher head0.297
Teacher spread0.207 · 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

Citations16
Published2022
Admission routes1
Has abstractno

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