Usefulness of the Berg Balance Scale in Stroke Rehabilitation: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: In a recent study of 655 physical therapists working with a stroke population, the Berg Balance Scale (BBS) was identified as the most commonly used assessment tool across the continuum of stroke rehabilitation. Given the widespread popularity of the BBS, it is important to critically appraise the BBS for its use with a stroke population. OBJECTIVE: The purposes of this study were to conduct a systematic review of the psychometric properties of the BBS specific to stroke and to identify strengths and weaknesses in its usefulness for stroke rehabilitation. RESULTS: Twenty-one studies examining the psychometric properties of the BBS with a stroke population were retrieved. Internal consistency was excellent (Cronbach alpha=.92-.98) as was interrater reliability (intraclass correlation coefficients [ICCs]=.95-.98), intrarater reliability (ICC=.97), and test-retest reliability (ICC=.98). Sixteen studies focused on validity and generally found excellent correlations with the Barthel Index, the Postural Assessment Scale for Stroke Patients, Functional Reach Test, the balance subscale of Fugl-Meyer Assessment, the Functional Independence Measure, the Rivermead Mobility Index (except for weight shift and step-up items), and gait speed. Berg Balance Scale scores predicted length of stay, discharge destination, motor ability at 180 days poststroke, and disability level at 90 days, but these scores were not predictive of falls. Eight studies focused on responsiveness; all reported moderate to excellent sensitivity. Three studies found floor or ceiling effects. DISCUSSION AND CONCLUSION: The BBS is a psychometrically sound measure of balance impairment for use in poststroke assessment. Given the floor and ceiling effects, clinicians may want to use the BBS in conjunction with other balance measures.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".