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Record W4306164626 · doi:10.1097/mrr.0000000000000552

Cognitive impairment predicts engagement in inpatient stroke rehabilitation

2022· article· en· W4306164626 on OpenAlexaboutno aff
Ryan Lowder, Abhishek Jaywant, Chaya B. Fridman, Joan Toglia, Michael W. O’Dell

Bibliographic record

VenueInternational Journal of Rehabilitation Research · 2022
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationStroke (engine)CognitionPhysical medicine and rehabilitationCognitive rehabilitation therapyPsychological interventionPhysical therapyMedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Patient engagement during inpatient rehabilitation is an important component of rehabilitation therapy, as lower levels of engagement are associated with poorer outcomes. Cognitive deficits may impact patient engagement during inpatient stroke rehabilitation. Here, we assess whether patient performance on the cognitive tasks of the 30-min National Institute of Neurologic Disorders and Stroke - Canadian Stroke Network (NINDS-CSN) screening battery predicts engagement in inpatient stroke rehabilitation. Prospective data from 110 participants completing inpatient stroke rehabilitation at an academic medical center were utilized for the present analyses. Cognitive functioning was assessed at inpatient stroke rehabilitation admission using the NINDS-CSN cognitive battery. Patient engagement was evaluated at discharge from an inpatient rehabilitation unit using the Hopkins Rehabilitation Engagement Rating Scale. The results demonstrate that the NINDS-CSN cognitive battery, specifically subtests measuring executive functioning, attention and processing speed, predicts patient engagement in inpatient stroke rehabilitation. Cognitively impaired patients undergoing rehabilitation may benefit from modifications and interventions to increase engagement and improve functional outcomes.

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)0.008
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.410
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.

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

Citations11
Published2022
Admission routes1
Has abstractyes

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