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Record W4402148582 · doi:10.1016/j.cccb.2024.100255

Cognition and mood in the first few months after stroke: relationship to stroke severity and dependency

2024· article· en· W4402148582 on OpenAlexaboutno aff
Ellen V. Backhouse, Lisa J Woodhouse, Fergus Doubal, Rosalind Brown, Philip M. Bath, Terence J. Quinn, Thompson Robinson, Hugh S. Markus, Richard J. McManus, John T. O’Brien, David J. Werring, Nikola Sprigg, Adrian Parry‐Jones, Rhian M. Touyz, Steven E. Williams, Yee‐Haur Mah, Hedley Emsley, Joanna M. Wardlaw

Bibliographic record

VenueCerebral Circulation - Cognition and Behavior · 2024
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsStroke (engine)MoodDependency (UML)CognitionPsychologyClinical psychologyMedicinePsychiatryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Cognitive decline and mood disorders are two major concerns of people affected by stroke. The extent to which cognition and mood relate to or are independent of stroke severity and post-stroke dependency are unclear. We examined the associations between stroke severity, global measures of cognition, mood and dependency up to 14-weeks post-stroke in a large national study of neurocognitive complications of stroke up to two years, the Rates, Risks and Routes to Reduce Vascular Dementia (R4VaD) study. R4VaD recruited patients with stroke of all subtypes and severities and collected clinical, cognitive and mood data at baseline (within six-weeks post-stroke), subacutely (6+/-2 weeks later; ie. maximum 14-weeks post-stroke) and 1 and 2 years. We measured baseline stroke severity (National Institute of Health Stroke Scale, NIHSS), pre-stroke and 14-week dependency (Modified Rankin Scale, mRS), cognition (Montreal Cognitive Assessment, MoCA; Modified Telephone Interview for Cognitive Status, TICS-m), and mood (Zung depression scale; Patient Health Questionnaire, PHQ; General Anxiety Disorder scale, GAD-7). We analysed baseline and 14-week cognition and mood using linear models with log-transformed NIHSS, adjusted for mRS, age, sex, education, hypertension, diabetes and smoking. We recruited 2441 participants (mean age=68.2 SD=13.5; 40% female; median NIHSS=2.0, IQR=0-4, range=0-24; median stroke onset to recruitment=6 days, IQR=3-13; median time to follow-up=6.6 weeks, IQR=6.0-7.9). Table 1 shows baseline and subacute cognition and mood scores. At baseline higher NIHSS associated with lower cognition (MoCA: β= -0.22, p<0.001; TICS-m: β= -0.19, p<0.001) and increased depressive symptoms (Zung β=0.09, p<0.01; PHQ: β=0.09, p<0.001) independent of prestroke mRS. At 6-week follow-up higher NIHSS associated with lower cognition (T-MoCA: β= -0.06, p=0.03; TICS-m: β= -0.09, p<0.01) independent of concurrent mRS. In this largest study of neurocognitive disorders after stroke, reduced cognition is associated with worse stroke severity 6-weeks post-stroke, independent of pre and post-stroke dependency. Low mood is not independent of post-stroke dependency. The extent to which these relationships remain at 1 and 2 years will be determined shortly. Improved prediction of clinical outcomes after stroke would benefit patient care; by allowing better prediction of cognitive and mood disorders, providing better prognostic information to patients.

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 score0.018
Threshold uncertainty score0.523

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 head0.025
GPT teacher head0.289
Teacher spread0.264 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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