A prospective study to establish the minimal clinically important difference of the Mini-BESTest in individuals with stroke
Bibliographic record
Abstract
OBJECTIVE: To determine the minimal clinically important difference of the Mini-BESTest in individuals' post-stroke. DESIGN: Prospective cohort study. SETTING: Outpatient stroke rehabilitation. SUBJECTS: Fifty outpatients with stroke with a mean (SD) age of 60.8 (9.4). INTERVENTION: Outpatients with stroke were assessed with the Mini-BESTest before and after a course of conventional rehabilitation. Rehabilitation sessions occurred one to two times/week for one hour and treatment duration was 1.3-42 weeks (mean (SD) = 17.4(10.6)). MAIN MEASURES: We used a combination of anchor- and distribution-based approaches including a global rating of change in balance scale completed by physiotherapists and patients, the minimal detectable change with 95% confidence, and the optimal cut-point from receiver operating characteristic curves. RESULTS: = 0.001). Mean change scores on the Mini-BESTest for patient and physiotherapist ratings of small change were 4.2 and 4.3 points, and 4.7 and 5.3 points for substantial change, respectively. The minimal detectable change with 95% confidence for the Mini-BESTest was 3.2 points. The minimally clinical importance difference was determined to be 4 points for detecting small changes and 5 points for detecting substantial changes. CONCLUSIONS: A change of 4-5 points on the Mini-BEST is required to be perceptible to clinicians and patients, and beyond measurement error. These values can be used to interpret changes in balance in stroke rehabilitation research and practice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".