Berg Balance Scale score at admission can predict walking suitable for community ambulation at discharge from inpatient stroke rehabilitation
Bibliographic record
Abstract
OBJECTIVE: This retrospective cohort study identified inpatient rehabilitation admission variables that predict walking ability at discharge and established Berg Balance Scale cut-off scores to predict the extent of improvement in walking. METHODS: Participants (n=123) were assessed for various cognitive and physical outcomes at admission to inpatient stroke rehabilitation. Multivariate logistic regression identified admission predictors of regaining community ambulation (gait speed ≥0.8 m/s) or unassisted ambulation (no physical assistance) after 4 weeks. Receiver operating characteristic curve analysis identified cut-off admission Berg Balance Scale scores. RESULTS: Mini-Mental State Examination (odds ratio (OR) 1.60, 95% confidence interval (95% CI) 1.19-2.14) was a significant predictor when coupled with admission walking speed for regaining community ambulation speed; stroke type (haemorrhagic/ischaemic) was a significant predictor (OR=0.19, 95% CI 0.05-0.77) when coupled with Berg Balance Scale (OR 1.14, 95% CI 1.09-1.20). Only Berg Balance Scale was a significant predictor of regaining unassisted ambulation (OR 1.11, 95% CI 1.05-1.17). A cut-off Berg Balance Scale score of 29 on admission predicts that an individual will go on to achieve community walking speed (n=123, area under the curve (AUC)=0.88, 95% CI 0.81-0.95); a cut-off score of 12 predicts a non-ambulator to regain unassisted ambulation (n=84, AUC 0.73, 95% CI 0.62-0.84). CONCLUSION: The Berg Balance Scale can be used at rehabilitation admission to predict the degree of improvement in walking for patients with 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".