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Record W2032964188 · doi:10.1177/0888439004266304

Training Mobility Tasks after Stroke with Combined Mental and Physical Practice: A Feasibility Study

2004· article· en· W2032964188 on OpenAlexafffund
Francine Malouin, Carol L. Richards, Julien Doyon, Johanne Desrosiers, Sylvie Belleville

Bibliographic record

VenueNeurorehabilitation and neural repair · 2004
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité LavalUniversité de SherbrookeUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersRéseau Provincial de Recherche en Adaptation-RéadaptationCanada Research Chairs
KeywordsPhysical medicine and rehabilitationPsychologyStroke (engine)Training (meteorology)RehabilitationPhysical therapyMedicineNeuroscience

Abstract

fetched live from OpenAlex

This study examines the potential of using mental practice (MP) to promote the learning of 2 mobility tasks in persons with stroke. Twelve patients were trained with MP to increase the loading of the affected limb while standing up from a chair and sitting down. Vertical forces were recorded using force plates under each foot and the chair. Changes in the loading of the affected limb and in task duration, immediately after 1 training session and 24 h later, served as outcomes. After training, the loading of the affected limb had increased (P < 0.001) during standing up (16.2%) and sitting down (17.9%), and the improvement remained significant 24 h later, indicating a learning effect. In contrast, the duration of the performance did not change with training. The results indicate that, in the early stage of learning with MP, changes in limb-loading strategies are a more sensitive measure of performance than is speed.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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 designNon-randomized trial
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

Citations116
Published2004
Admission routes2
Has abstractyes

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