Longitudinal Analysis of Balance Confidence in Individuals With Stroke Using a Multilevel Model for Change
Bibliographic record
Abstract
BACKGROUND: Confidence about balance may be an important factor affecting self-efficacy for daily activities after stroke. OBJECTIVE: The authors investigated whether confidence changes and the parameters that may predict changes in self-perceived balance within the first year of community reintegration. METHODS: In this prospective study, 98 hemiparetic participants who could walk at least 10 m independently were assessed 1, 3, 6, and 12 months postdischarge from rehabilitation by the Berg Balance Scale, Timed Up and Go, 6-minute walking distance, and depression and anxiety scales. The 98 age-/sex-matched controls were assessed at enrollment and the same intervals. Multilevel modeling was used to determine if assessed covariates predicted changes in balance confidence by the Activities-Specific Balance Confidence Scale. RESULTS: Stroke subjects had lower initial balance confidence than controls and showed a statistically significant increase in confidence over 1 year at each assessment. Poor balance and high state anxiety were associated with lower balance confidence. Significant interactions were observed between greater depression, along with more impaired walking capacity and balance tests, in predicting lower change trajectories in confidence. Falls predicted a reduced rate of improvement. CONCLUSIONS: Balance confidence after stroke remained lower than controls over the first year. Physical therapies and supportive interventions designed to treat confidence should be initiated at discharge from inpatient rehabilitation.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".