Responsiveness of the mini-balance evaluation systems test, dynamic gait index, Berg balance scale, and performance-oriented mobility assessment in parkinson’s disease
Bibliographic record
Abstract
Commonly used clinical tools like the Mini-Balance Evaluation Systems Test (Mini-BESTest), Dynamic Gait Index (DGI), Berg Balance Scale (BBS), and Performance-Oriented Mobility Assessment (POMA) are essential for assessing balance impairments in people with Parkinson’s disease (PwPD), but their clinical relevance depends on understanding the Minimal Important Difference (MID) and Minimal Detectable Change (MDC). To assess the responsiveness of the Mini-BESTest, DGI, BBS, and POMA in PwPD. This prospective cohort study included 130 PwPD who were recruited. Participants underwent baseline and post-intervention assessments using the Mini-BESTest, DGI, BBS, and POMA. The intervention consisted of task-oriented exercises focused on motor re-learning to improve functional balance. Anchor and distribution methods were used to determine the MID (the receiver-operating characteristic(ROC)-based (MID ROC ), the predictive modeling method (MID pred ), and the MIDpred-based method adjusted by the rate of improvement (MID adj )) and MDC. ROC-derived MID(MID ROC ) exceeded 1 point for all scales, but improvement-rate-adjusted MID (MID adj ) provided more conservative estimates: Mini-BESTest (2.96 points), DGI (2.29), BBS (1.97), and POMA (0.83). Predictive modeling (MID pred ) yielded higher thresholds (e.g., 4.0 for Mini-BESTest), likely reflecting baseline variability. The MDCs was 1.9, 2.0, 1.0, and 2.8 points for the Mini-BESTest, DGI, BBS, and POMA, respectively. The findings suggest that the Mini-BESTest, DGI, BBS, and POMA are responsive outcome measures for PD. This study’s results can provide valuable insights into interpreting changes in patient performance, thereby supporting clinical interventions and facilitating research planning.
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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 | 0.004 | 0.016 |
| 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".