Understanding Approaches to Balance Assessment in Physical Therapy Practice for Elderly Inpatients of a Rehabilitation Hospital
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".