The Community Balance and Mobility Scale
Bibliographic record
Abstract
PURPOSE: Many patients participating in cardiac rehabilitation (CR) programs have decreased balance. This is a concern, as it may affect their ability to optimally perform physical exercise in CR and thus decrease CR efficacy. Despite this concern, balance is typically not assessed as part of CR intake. This may be attributable to the fact that a suitable balance assessment tool has not been identified for higher-functioning CR patients. A potential solution to this issue is using the Community Balance and Mobility Scale (CBMS), which has been used to assess balance in higher-functioning clinical populations; however, its use in a CR population has never been investigated. Therefore, the purpose of this study was to determine the reliability and validity of the CBMS for assessing balance in CR patients. METHODS: Fifty-three participants were recruited from local CR programs to perform the CBMS. Dynamic posturography was also measured in a subset of participants (n = 31) using the Limits of Stability (LOS) test. RESULTS: Analysis of CBMS scores revealed that the mean CBMS score was 61.9 ± 16.2 (out of 96) and that no floor or ceiling effects were observed for any participants. CBMS scores were significantly correlated with the LOS results (0.41-0.53). Interrater reliability between novice and expert testers was strong (r = 0.95), with all differences falling within the 95% limits of agreement. CONCLUSION: Overall, these results suggest that the CBMS is a valid tool to measure balance in CR patients and can be reliably administered by health care professionals with minimal training.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".