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Record W2972903516 · doi:10.1177/1747493019873600

Cognition in stroke rehabilitation and recovery research: Consensus-based core recommendations from the second Stroke Recovery and Rehabilitation Roundtable

2019· article· en· W2972903516 on OpenAlexafffund
Matthew W. McDonald, Sandra E. Black, David A. Copland, Dale Corbett, Rick M. Dijkhuizen, Tracy D. Farr, Matthew S. Jeffers, Raj N. Kalaria, Frini Karayanidis, Alexander Leff, Jess Nithianantharajah, Sarah T. Pendlebury, Terence J. Quinn, Andrew N. Clarkson, Michael O’Sullivan

Bibliographic record

VenueInternational Journal of Stroke · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreHeart and Stroke FoundationUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNational Institute for Health and Care Research
KeywordsRehabilitationStroke (engine)CognitionMedicinePhysical medicine and rehabilitationCognitive rehabilitation therapyPsychological interventionStroke recoveryPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Cognitive impairment is an important target for rehabilitation as it is common following stroke, is associated with reduced quality of life and interferes with motor and other types of recovery interventions. Cognitive function following stroke was identified as an important, but relatively neglected area during the first Stroke Recovery and Rehabilitation Roundtable (SRRR I), leading to a Cognition Working Group being convened as part of SRRR II. There is currently insufficient evidence to build consensus on specific approaches to cognitive rehabilitation. However, we present recommendations on the integration of cognitive assessments into stroke recovery studies generally and define priorities for ongoing and future research for stroke recovery and rehabilitation. A number of promising interventions are ready to be taken forward to trials to tackle the gap in evidence for cognitive rehabilitation. However, to accelerate progress requires that we coordinate efforts to tackle multiple gaps along the whole translational pathway.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.289
metaresearch head score (Gemma)0.339
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.289
Threshold uncertainty score0.877

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2890.339
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0140.016
Bibliometrics0.0140.010
Science and technology studies0.0040.005
Scholarly communication0.0130.012
Open science0.0160.016
Research integrity0.0230.025
Insufficient payload (model declined to judge)0.0070.005

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 head0.046
GPT teacher head0.353
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
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

Citations89
Published2019
Admission routes2
Has abstractyes

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