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Record W4412711514 · doi:10.1101/2025.07.24.25332137

Optimal reactive balance training characteristics post-stroke: secondary analysis of a randomized controlled trial

2025· preprint· en· W4412711514 on OpenAlexafffund
Júlia O. Faria, Cynthia J. Danells, Elizabeth L. Inness, Avril Mansfield

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsSunnybrook Health Science CentreToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorToronto Rehabilitation InstituteCanadian Institutes of Health ResearchOntario Innovation Trust
KeywordsBalance (ability)Physical medicine and rehabilitationPhysical therapyStroke (engine)Chronic strokeRandomized controlled trialBalance trainingMedicineDynamic balancePsychologyRehabilitationInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Background and purpose Reactive balance training (RBT) has shown promise for enhancing reactive balance control and reducing falls post-stroke. However, the optimal training parameters (e.g., intensity, duration) are unknown. This study aimed to investigate the relationship between different reactive balance training characteristics and improvements in reactive balance control and fall rates. Methods People with chronic stroke completed up to 12 one-hour reactive balance training sessions, twice per week. Training included experiencing losses of balance due to internal or external perturbations while performing voluntary tasks. The tasks were of four types: stable, quasi-mobile, mobile, and unpredictable, each with choice of three difficulty levels (normal, increased, or reduced). We analyzed the relationships between training characteristics (total number of perturbations, difficulty levels, perceived level challenge, and success rate) and fall rates post-training and changes in the reactive balance control sub-score of the mini-Balance Evaluation Systems Test (mini-BESTest). Results A higher number of perturbations was significantly associated with better post-intervention reactive balance scores on the mini-BEST (p=0.010). There were no significant associations with any other training characteristics and post-intervention mini-BEST Scores. For falls in daily life there was no significant association between any training characteristic. Discussion Greater exposure to RBT was associated with improvements in reactive balance control among individuals with chronic stroke. Participants who completed more sessions, and consequently experienced more perturbations, achieved better outcomes. These findings highlight the importance of sufficient training volume, suggesting that a higher number of perturbations may be optimizing the effects of RBT in stroke rehabilitation. Trial registration ISRCTN05434601

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.025
GPT teacher head0.350
Teacher spread0.324 · 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 designRandomized 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

Citations0
Published2025
Admission routes2
Has abstractyes

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