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Record W4406993545 · doi:10.1161/str.56.suppl_1.wp115

Abstract WP115: Influence of Cognitive Factors on Motor Performance Early After Stroke

2025· article· en· W4406993545 on OpenAlexaboutno aff
Isabel Cardoso Ferreira, Min‐Keun Song, Marc D. Feldman, Lorie Brinkman, Christina Holl, Tiffany Nguyen‐Vu, Anne Schwarz, Nina Soleimani, Andrea Stehman, Brittany M. Young, Steven C. Cramer

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)CognitionPhysical medicine and rehabilitationPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Background: Cognitive factors influence motor performance, but often this is not considered when testing motor function. Here we examined this issue using 3 versions of the Box and Blocks Test (BBT), a measure of arm motor function. First, we developed 2 new, briefer versions of BBT and then tested their validity. Second, we hypothesized that cognitive factors would be more strongly related to versions of the BBT that require a longer period of testing. Methods: In 71 patients <=30 days post-stroke, 3 BBT versions were scored. BBT60 is the standard one; subjects move as many blocks as possible over a divider in 60 sec. We also counted # blocks moved in the first 10 sec (BBT10) and time to move the first 3 blocks (3-BBT). Other testing included 2 motor scores, Fugl Meyer (FM) and 9-Hole Peg (9HP); and 2 cognitive scores, Trail Making Test-A (TMT-A) and Montreal Cognitive Assessment (MoCA). First, we tested the validity of the briefer BBT versions (3-BBT and BBT10) against BBT60 and FM. Second, we compared all BBT versions to measures of sustained attention (TMT-A) and cognitive function (MoCA). Comparisons used Spearman’s rank correlation coefficient (threshold p<0.05). Results: Mean time post-stroke was 13.1±5.9 days; age, 66.9±14.9 yrs; 40.8% female. Median BBT scores: 3-BBT, 17.4 sec; BBT10, 2 blocks; and BBT60, 10 blocks. Other median scores: FM, 36 points, MoCA, 21 points; 9HP, 1 peg in 60 sec; TMT-A, 49.5 sec for 25 targets. Scores on 3-BBT (r=-0.93, p<0.0001) and BBT10 (r=0.95, p<0.0001) showed strong validity with BBT60 scores. Each BBT version, respectively, was related to FM score (r=-0.81, r=0.77, r=0.82; all p<0.0001) and to 9HP score (r=-0.77, r=0.82, r=0.82; all p<0.0001). TMT-A was not significantly related to 3-BBT (r=0.20, p=0.09), but was related to BBT10 (r=-0.24, p=0.04) and BBT60 (r=-0.24, p=0.04). MoCA was not significantly related to 3-BBT (r=-0.15, p=0.21) or BBT60 (r=0.20, p=0.09), but was to BBT10 (r=0.26, p=0.03). Conclusions: The 3-BBT and BBT10 are both valid measures of arm motor function in subacute stroke. Each is quicker and more easily assessed than BBT60. The BBT10 and BBT60 were more strongly related to TMT-A, and BBT60 to MoCA, suggesting that these 2 BBT versions are more influenced by attention or cognition compared to 3-BBT. All 3 BBT measures are valid; choice of which to use depends on whether the goal is to measure motor function with less (3-BBT) or more (BBT10 and BBT60) influence of attention or cognitive factors.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.269
Teacher spread0.260 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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