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Record W4246990749 · doi:10.21203/rs.3.rs-120140/v1

Associations between Post-Stroke Motor and Cognitive Function: A Cross-Sectional Study

2020· preprint· en· W4246990749 on OpenAlexaboutno aff
Marte Stine Einstad, Ingvild Saltvedt, Stian Lydersen, Marie Ursin, Ragnhild Munthe‐Kaas, Hege Ihle‐Hansen, Anne‐Brita Knapskog, Torunn Askim, Mona K. Beyer, Halvor Næss, Yngve Müller Seljeseth, Hanne Ellekjær, Pernille Thingstad

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersFaculty of Medicine and Health, University of SydneySt. Olavs Hospital Universitetssykehuset i TrondheimHaukeland UniversitetssjukehusNasjonalforeningen for FolkehelsenNorges Teknisk-Naturvitenskapelige Universitet
KeywordsMontreal Cognitive AssessmentCognitionStroke (engine)MedicineModified Rankin ScalePhysical medicine and rehabilitationPhysical therapyNorwegianCross-sectional studyPsychologyCognitive impairmentPsychiatryIschemic stroke

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Although both motor and cognitive impairments are frequently observed following stroke, these are often managed as distinct entities, and there is little evidence regarding how they are related. The aim of this study was to describe the prevalence of concurrent motor and cognitive impairments three months after stroke and to examine how motor performance was associated with memory, executive function and global cognition. METHODS: The Norwegian Cognitive Impairment After Stroke (Nor-COAST) study is a prospective multicentre cohort study including patients hospitalized with acute stroke. The National Institutes of Health Stroke Scale (NIHSS) was used to measure stroke severity, and function was measured by the Modified Rankin Scale (mRS). Motor and cognitive functions were assessed three months post-stroke using the Montreal Cognitive Assessment (MoCA), Trail Making Test Part B (TMT-B), 10-Word List Recall (10WLR), Short Physical Performance Battery (SPPB), dual-task cost (DTC) and grip strength. Cut-offs were set according to current recommendations. Associations were examined using linear regression with the cognitive tests as dependent variables and motor domains as covariates, adjusted for age, sex, education and stroke severity. RESULTS: Of 567 participants included, 242 (43%) were women, mean (SD) age was 72.2 (11.7) years, 416 (75%) had an NIHSS score ≤ 4 and 475 (84%) had an mRS score of ≤ 2. Prevalence of concurrent motor and cognitive impairment ranged from 9.5% for DTC and 10WLR to 22.9% for grip strength and TMT-B. The SPPB was associated with the MoCA (regression coefficient B=0.465, 95%CI [0.352, 0.578]), TMT-B (B=-9.494, 95%CI [-11.726, -7.925]) and 10WLR (B=0.132, 95%CI [0.054, 0.211]). Grip strength was associated with the MoCA (B=0.075, 95%CI [0.039, 0.112]), TMT-B (B=-1.972, 95%CI [-2.672, -1.272]) and 10WLR (B=0.041, 95%CI [0.016, 0.066]). Higher DTC was associated with more time needed to complete the TMT-B (B=0.475, 95%CI [0.075, 0.875]) but not with the MoCA or 10WLR. CONCLUSION: Three months after suffering mainly minor strokes, 30–40% of participants had motor or cognitive impairments, while 20% had concurrent impairments. Motor performance was associated with memory, executive function and global cognition. The identification of concurrent impairments could be relevant for preventing functional decline. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT02650531

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.105
GPT teacher head0.443
Teacher spread0.337 · 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
Published2020
Admission routes1
Has abstractyes

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