Psychometric properties of Brief‐Balance Evaluation Systems Test (Brief‐<scp>BEST</scp>est) in evaluating balance performance in individuals with chronic stroke
Bibliographic record
Abstract
Abstract Objective To examine the psychometric properties of the Brief‐Balance Evaluation Systems Test (Brief‐BESTest) in individuals with chronic stroke. Materials and Methods This was an observational study with repeated measurements involving 50 participants with chronic stroke [mean (SD) age: 59.2 (7.3) years]. Each participant with stroke was evaluated with the Brief‐BESTest, Berg balance scale (BBS), Postural Assessment Scale for Stroke Patients (PASS), Fugl‐Meyer Motor Assessment (FMA), Chedoke‐McMaster Stroke Assessment (CMSA), Montreal Cognitive Assessment (MoCA), and Geriatric Depression Scale (GDS). Two raters (rater 1 and 2) provided the Brief‐BESTest scores of the first 27 participants independently to establish inter‐rater reliability. After 15 min of rest, the same 27 participants were evaluated with the Brief‐BESTest again by rater 1 to establish intra‐rater reliability. The Brief‐BESTest scores of the stroke group were also compared with those of the control group [n = 27, mean (SD) age: 56.7 (7.7) years]. Results The Brief‐BESTest had no substantial floor and ceiling effects, good intra‐rater (ICC2,1 = 0.974) and inter‐rater (ICC2,1 = 0.980) reliability and internal consistency (Cronbach's alpha = 0.818). The minimal detectable change at 95% confidence level was 2 points. The Brief‐BESTest showed moderate to very strong correlations with other balance (BBS and PASS) and motor impairment (FMA, CMSA) measures (rs = .547–.911, p < .001), thus revealing good concurrent and convergent validity. Its correlation with measures that evaluated other constructs was weaker (MoCA: rs = .437, p = .002) or non‐significant (GDS: rs = −0.152, p = .292), thus showing good discriminant validity. Good known‐groups validity was established, as the Brief‐BESTest was effective in distinguishing participants with stroke from controls (cutoff score: <18, area under curve: 0.942), and individuals with stroke who required assistive device for their outdoor mobility from those who did not (cutoff score <14, area under curve: 0.810). Conclusions The Brief‐BESTest has good reliability and validity in assessing balance function in individuals with chronic stroke.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".