Minimal clinically important difference of the Brief-BESTest in COPD
Bibliographic record
Abstract
Balance training within pulmonary rehabilitation (PR) improves balance of people with COPD. Brief-Balance Evaluation System Test (Brief-BESTest) is a balance test commonly used to assess balance; however, the clinical interpretability of its improvements is impaired by the lack of established minimal clinically important differences (MCIDs). This study established the MCID of the Brief-BESTest in people with COPD. The MCID was computed using anchor (mean changes, linear regressions) and distribution (0.5*SD, standard error of measurement - SEM, 1.96*SEM and minimal detectable change) based methods. Changes in the 6-minute walk test (6MWT) and the modified Medical Research Council dyspnoea scale (mMRC) were assessed pre/post a 12-week PR programme, with exercise/balance training 2x/week, and used as anchors. Pooled MCIDs were computed using the arithmetic weighted mean (2/3 for anchor- and 1/3 for distribution-based methods). 68 people with COPD (69±8 yrs; 51 male; FEV1 49±17pp) completed the PR programme. Significant correlations were found between the Brief-BESTest and the 6MWT (r=0.34; p=0.005), and the mMRC (r=-0.33; p=0.007). The pooled MCID was 3.3 points (Fig. 1). An improvement ≥3 points in the Brief-BESTest seems to be clinically meaningful in people with COPD following PR with balance training. The estimated MCID will aid health professionals to interpret the effects of PR on balance performance and guide tailored interventions.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".