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Record W1953224847 · doi:10.4137/rpo.s20363

Force Plate Assessment of Quiet Standing Balance Control: Perspectives on Clinical Application within Stroke Rehabilitation

2015· article· en· W1953224847 on OpenAlexaff
Avril Mansfield, Elizabeth L. Inness

Bibliographic record

VenueRehabilitation Process and Outcome · 2015
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsHeart and Stroke FoundationToronto Rehabilitation InstituteUniversity of TorontoUniversity Health NetworkSunnybrook Health Science Centre
Fundersnot available
KeywordsCenter of pressure (fluid mechanics)RehabilitationQUIETBalance (ability)Physical medicine and rehabilitationForce platformStroke (engine)Physical therapyMedicineEngineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Assessment of balance control is essential to guide physical rehabilitation poststroke. However, current observational assessment tools available to physiotherapists provide limited information about underlying dyscontrol. This paper describes a force plate-based assessment of quiet standing balance control that we have implemented for individuals attending inpatient stroke rehabilitation. The assessment uses two force plates to measure location of ground reaction forces to maintain stability in quiet standing in five conditions (eyes open, eyes closed, standing symmetrically, and maximal loading on the less-affected and more-affected limbs). Measures of interest are variability of the centers of pressure under each foot and both feet combined, weight-bearing asymmetry, and correlation of center of pressure fluctuations between limbs. We present representative values for the above-mentioned measures and case examples to illustrate how the assessment can reveal patient-specific balance control problems and direct treatment. We identify limitations to our current assessment and recommendations for future research.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.036
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.462
Teacher spread0.418 · 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.

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

Citations34
Published2015
Admission routes1
Has abstractyes

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