Which Balance Subcomponents Distinguish Between Fallers and Non-Fallers in People with COPD?
Bibliographic record
Abstract
Stephanie Chauvin,1 Renata Kirkwood,1 Dina Brooks,1,2 Roger S Goldstein,2– 4 Marla K Beauchamp1,2,5 1School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada; 2Department of Respiratory Medicine, West Park Healthcare Centre, Toronto, Ontario, Canada; 3Department of Physical Therapy, University of Toronto, Toronto, Ontario, Canada; 4Department of Medicine, University of Toronto, Toronto, Ontario, Canada; 5Firestone Institute for Respiratory Health, St. Joseph’s Healthcare, Hamilton, Ontario, CanadaCorrespondence: Marla K BeauchampMcMaster University, 1400 Main Street West, Hamilton, ON L8S 1C7, IAHS 430 CanadaTel +1 905 525-9140 x 21732Email beaucm1@mcmaster.caRationale: Chronic obstructive pulmonary disease (COPD) is an increasingly prevalent lung disease linked to dysfunctional balance and an increased risk of falls. The Balance Evaluation Systems Test (BESTest) evaluates the six underlying subcomponents of functional balance. The aim of this study was to determine the specific balance subcomponents and cut-off scores that discriminate between fallers and non-fallers with COPD to guide fall risk assessment and prevention.Methods: A secondary analysis of cross-sectional data from two prior studies in COPD was performed. Independent samples t-tests were used to explore the differences in the BESTest sub-system scores between fallers and non-fallers. Receiver operating characteristic curves were used to determine the optimal subcomponent cut-off scores that identified fallers, and the area under the curve (AUC) was used to assess test accuracy.Results: Data from 72 subjects with COPD (mean age, 70.3 ± 7.4y; mean forced expiratory volume in 1 second, 38.9 ± 15.8% predicted) were analyzed. Two BESTest subcomponents, stability limits/verticality (fallers: 75.4%, non-fallers: 83.8%; p=0.002) and postural responses (fallers: 67.5%, non-fallers: 79.7%; p=0.008) distinguished between fallers and non-fallers. Stability limits/verticality had an AUC of 0.70 and optimal cut-off score of 73.8% for identifying fallers; postural responses had an AUC of 0.67 and optimal cut-off score of 69.4%.Conclusion: The stability limits/verticality and postural responses subcomponents of the BESTest distinguished between fallers and non-fallers with COPD. The stability limits/verticality subcomponent can also be used to identify whether an individual with COPD is at risk of falling using a cut-off score of 73.8%. These findings suggest that specific subcomponents of balance could be targeted to optimize fall risk assessment and prevention in COPD.Keywords: BESTest, chronic obstructive pulmonary disease, balance, falls, 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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".