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Record W1965554726 · doi:10.3138/ptc.2012-57

Understanding Approaches to Balance Assessment in Physical Therapy Practice for Elderly Inpatients of a Rehabilitation Hospital

2013· article· en· W1965554726 on OpenAlexafffundvenueabout
Tanner Gervais, Nicole Burling, Justin Krull, Carrie Lugg, Maria Lung, Sharon E. Straus, Susan Jaglal, Kathryn M. Sibley

Bibliographic record

VenuePhysiotherapy Canada · 2013
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science CentreToronto Rehabilitation InstituteUniversity of Toronto
FundersUniversity of TorontoToronto Rehabilitation Institute
KeywordsBalance (ability)MedicineRehabilitationDeconditioningBerg Balance ScalePhysical therapyPhysical medicine and rehabilitationBalance problems

Abstract

fetched live from OpenAlex

PURPOSE: Balance is a critical modifiable risk factor for falls in older adults. The purpose of this study was to identify the physiotherapy assessment tools used to evaluate balance in clinical practice and to determine the components of balance assessed through the use of standardized and/or other measures. METHODS: A retrospective chart review was performed on 250 patients aged 65 and older undergoing in-patient rehabilitation with a clinical diagnosis associated with a balance impairment (stroke, musculoskeletal conditions, lower limb amputation, deconditioning, or cardiac surgery) at a rehabilitation hospital in Ontario. RESULTS: All patients received at least one assessment that incorporated a balance component. Standardized balance measures were performed for 73% of patients; the timed up-and-go test (48%) and Berg Balance Scale (36%) were most commonly used for this assessment. Reactive movement strategies and cognitive processing were the least commonly assessed components of balance. Balance was assessed least often among people with cardiac surgery. CONCLUSIONS: Considerable variation exists in the assessment of balance, and the data suggest that not all relevant components are equally considered. Future research should be conducted in other clinical settings and should include other health care practitioners' assessments to develop a more complete understanding of current balance assessment procedures.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.569
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.086
GPT teacher head0.380
Teacher spread0.294 · 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

Citations21
Published2013
Admission routes4
Has abstractyes

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