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Record W4396711707 · doi:10.1093/braincomms/fcae133

Impact of long-term white matter hyperintensity changes on mobility and dexterity

2024· article· en· W4396711707 on OpenAlexfundaboutno aff
Angela C.C. Jochems, Susana Muñoz Maniega, Francesca M. Chappell, Úna Clancy, Carmen Arteaga, Daniela Jaime García, Olivia KL Hamilton, Will Hewins, Rachel Locherty, Ellen V. Backhouse, Gayle Barclay, Charlotte Jardine, Donna McIntyre, Iona Gerrish, Yajun Cheng, Xiaodi Liu, Junfang Zhang, Agniete Kampaite, Eleni Sakka, María Valdés Hernández, Stewart Wiseman, Michael Stringer, Michael J. Thrippleton, Fergus Doubal, Joanna M. Wardlaw

Bibliographic record

VenueBrain Communications · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersEdinburgh ImagingBiotechnology and Biological Sciences Research CouncilMedical Research CouncilDirectorate for Biological SciencesUK Dementia Research InstituteAlzheimer’s SocietyChina Scholarship CouncilWellcome TrustAlzheimer's SocietyChief Scientist OfficeConsejo Nacional de Ciencia y TecnologíaFondation LeducqUniversity of EdinburghEconomic and Social Research CouncilStroke AssociationScottish Funding CouncilMedical Research Council CanadaEdinburgh and Lothians Health FoundationBritish Heart FoundationMrs Gladys Row Fogo Charitable Trust
KeywordsHyperintensityStroke (engine)MedicineModified Rankin ScalePhysical therapyLogistic regressionPsychologyPhysical medicine and rehabilitationCardiologyInternal medicineMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

White matter hyperintensities (WMH), a common feature of cerebral small vessel disease, are related to worse clinical outcomes after stroke. We assessed the impact of white matter hyperintensity changes over 1 year after minor stroke on change in mobility and dexterity, including differences between the dominant and non-dominant hands and objective in-person assessment versus patient-reported experience. We recruited participants with lacunar or minor cortical ischaemic stroke, performed medical and cognitive assessments and brain MRI at presentation and at 1 year. At both time points, we used the timed-up and go test and the 9-hole peg test to assess mobility and dexterity. At 1 year, participants completed the Stroke Impact Scale. We ran two linear mixed models to assess change in timed-up and go and 9-hole peg test, adjusted for age, sex, stroke severity (National Institutes of Health Stroke Scale), dependency (modified Rankin Score), vascular risk factor score, white matter hyperintensity volume (as % intracranial volume) and additionally for 9-hole peg test: Montreal cognitive assessment, hand (dominant/non-dominant), National Adult Reading Test (premorbid IQ), index lesion side. We performed ordinal logistic regression, corrected for age and sex, to assess relations between timed-up and go and Stroke Impact Scale mobility, and 9-hole peg test and Stroke Impact Scale hand function. We included 229 participants, mean age 65.9 (standard deviation = 11.13); 66% male. 215/229 attended 1-year follow-up. Over 1 year, timed-up and go time increased with aging (standardized β [standardized 95% Confidence Interval]: 0.124[0.011, 0.238]), increasing National Institutes of Health Stroke Scale (0.106[0.032, 0.180]), increasing modified Rankin Score (0.152[0.073, 0.231]) and increasing white matter hyperintensity volume (0.176[0.061, 0.291]). Men were faster than women (-0.306[0.011, 0.238]). Over 1 year, slower 9-hole peg test was related to use of non-dominant hand (0.290[0.155, 0.424]), aging (0.102[0.012, 0.192]), male sex (0.182[0.008, 0.356]), increasing National Institutes of Health Stroke Scale (0.160 [0.094, 0.226]), increasing modified Rankin Score (0.100[0.032, 0.169]), decreasing Montreal cognitive assessment score (-0.090[-0.167, -0.014]) and increasing white matter hyperintensity volume (0.104[0.015, 0.193]). One year post-stroke, Stroke Impact Scale mobility worsened per second increase on timed-up and go, odds ratio 0.67 [95% confidence interval 0.60, 0.75]. Stroke Impact Scale hand function worsened per second increase on the 9-hole peg test for the dominant hand (odds ratio 0.79 [0.71, 0.86]) and for the non-dominant hand (odds ratio 0.88 [0.83, 0.93]). Decline in mobility and dexterity is associated with white matter hyperintensity volume increase, independently of stroke severity. Mobility and dexterity declined more gradually for stable and regressing white matter hyperintensity volume. Dominant and non-dominant hands might be affected differently. In-person measures of dexterity and mobility are associated with self-reported experience 1-year post-stroke.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.043
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.350
Teacher spread0.309 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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