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Record W2951619206 · doi:10.1177/0891988719853044

The Impact of Cognitive Impairment on Poststroke Outcomes: A 5-Year Follow-Up

2019· article· en· W2951619206 on OpenAlexaboutno aff
Daniela Rohde, Eva Gaynor, Margaret Large, Lisa Mellon, Patricia Hall, Linda Brewer, Kathleen Bennett, David Williams, Eamon Dolan, Elizabeth Callaly, Anne Hickey

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersHealth Research Board
KeywordsCognitive impairmentCognitionDementiaPhysical medicine and rehabilitationPsychologyMedicineStroke (engine)GerontologyCognitive disorderPsychiatryDisease

Abstract

fetched live from OpenAlex

AIM: To explore the impact of cognitive impairment poststroke on outcomes at 5 years. METHODS: Five-year follow-up of the Action on Secondary Prevention Interventions and Rehabilitation in Stroke (ASPIRE-S) prospective cohort. Two hundred twenty-six ischemic stroke survivors completed Montreal Cognitive Assessments at 6 months poststroke. Outcomes at 5 years included independence in activities of daily living, receipt of informal care, quality of life, and depressive symptoms. Data were analyzed using logistic and linear regression models. Adjusted odds ratios (ORs; 95% confidence interval [CI]) and β coefficients (95% CI) are reported. RESULTS: One hundred one stroke survivors were followed up at 5 years. Cognitive impairment at 6 months was independently associated with worse quality of life (B [95% CI]: -0.595 [-0.943 to -0.248]), lower levels of independence (B [95% CI]: -3.605 [-5.705 to -1.505]), increased likelihood of receiving informal care (OR [95% CI]: 6.41 [1.50-27.32]), and increased likelihood of depressive symptoms (OR [95% CI]: 4.60 [1.22-17.40]). Conclusion: Cognitive impairment poststroke is associated with a range of worse outcomes. More effective interventions are needed to improve outcomes for this vulnerable group of 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 score0.025
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.007
GPT teacher head0.285
Teacher spread0.278 · 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

Citations73
Published2019
Admission routes1
Has abstractyes

Explore more

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