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Cognitive change following stroke and its impact on long-term care

2016· article· en· W2555400149 on OpenAlexaboutno aff
Fiona Orme, Birgit Gurr

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2016
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentStroke (engine)CognitionMedicinePsychological interventionAcute strokePhysical therapyCognitive impairmentAcute carePhysical medicine and rehabilitationHealth carePsychiatryEmergency department

Abstract

fetched live from OpenAlex

Aims/Background: The outcomes of cognitive assessments after stroke provide important information for the implementation of immediate specialist interventions. This study investigated the trajectory of cognitive changes in an acute stroke sample. This was done by examining the relationship between cognitive changes following stroke as determined by the Montreal Cognitive Assessment (MoCA) and patients' discharge locations (own home, home with social care package, and placement). Method: The data of 124 hospitalised patients with acute stroke was retrospectively analysed. Patients' age ranged from 51 to 96 years, 77 patients were women and 47 were men. Data of post-stroke cognitive outcomes as represented by the MoCA results and discharge location were collected. Results: Patients' MoCA Mean outcome was 12.12 and Standard Deviation 7.77. Significant differences were found between patients with low post-stroke cognitive functioning, requiring more intensive care after discharge (i.e. placement), and those with better cognition, who subsequently were discharged home. The MoCA subdomain ‘Concentration and Calculation’ was associated most with discharge location. The study outcomes can potentially improve the efficiency of very early interventions and hospital discharge plans for acute stroke 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 score0.370
Threshold uncertainty score0.172

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 head0.027
GPT teacher head0.394
Teacher spread0.367 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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