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Record W2765862835 · doi:10.3389/fnagi.2017.00343

Post-stroke Fatigue and Depressive Symptoms Are Differentially Related to Mobility and Cognitive Performance

2017· article· en· W2765862835 on OpenAlexaffabout
Bradley J. MacIntosh, Jodi D. Edwards, Mani Kang, Hugo Cogo‐Moreira, Joyce L. Chen, George Mochizuki, Nathan Herrmann, Walter Swardfager

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2017
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsOttawa HospitalHeart and Stroke FoundationToronto Rehabilitation InstituteUniversity Health NetworkUniversity of TorontoSunnybrook Hospital
Fundersnot available
KeywordsStroke (engine)CognitionCenter for Epidemiologic Studies Depression ScaleMontreal Cognitive AssessmentDepression (economics)MedicinePhysical therapyPhysical medicine and rehabilitationPsychologyDepressive symptomsClinical psychologyPsychiatryCognitive impairment

Abstract

fetched live from OpenAlex

Background: Fatigue and depressive symptoms are common and often inter-related stroke sequelae. This study investigates how they contribute, directly or indirectly, to mobility and cognitive outcomes within 6 months of stroke. Methods: Participants were recruited from 4 stroke centres in Ontario, Canada. Post-stroke fatigue was assessed using the Fatigue Assessment Scale (FAS). Depressive symptoms were screened using the Center for Epidemiological Studies Scale for Depression (CES-D). Factor analyses were used to construct scores from mobility (distance travelled during a 2-minute walk test, Chedoke-McMaster Stroke Assessment leg score [CMSA-leg], and Berg Balance Scale [BBS] total score) and cognitive (Montreal Cognitive Assessment [MoCA], Trail-Making Tests A and B, and five-word free recall) tests. Direct associations were assessed in linear regression models and indirect effects were assessed in path models. Covariates were age, sex, education, antidepressant use, days since stroke and stroke severity. Results: CES-D and FAS scores were highly correlated (r>0.51, p<0.0001). Depressive symptoms were associated with cognition (β=-0.184, p=0.04) and indirectly with mobility, mediated by fatigue (indirect effect = -0.0142, 95% CI: -0.0277 to -0.0033). Fatigue was associated with mobility (β=-0.253, p=0.01), and indirectly with cognition, mediated by depressive symptoms (indirect effect = -0.0113, 95% CI: -0.0242 to -0.0023). Conclusions: Fatigue and depressive symptoms contribute distinctly to cognitive and mobility impairments post-stroke. Fatigue was associated with poorer lower limb motor function, and with cognition indirectly via depressive symptoms.

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)0.001
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 score0.015
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.277
Teacher spread0.263 · 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

Citations70
Published2017
Admission routes2
Has abstractyes

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