Measuring Balance and Mobility after Traumatic Brain Injury: Validation of the Community Balance and Mobility Scale (CB&M)
Bibliographic record
Abstract
PURPOSE: To further investigate the construct validity of the Community Balance and Mobility Scale (CB&M), developed for ambulatory individuals with traumatic brain injury (TBI). METHODS: A convenience sample of 35 patients with TBI (13 in-patients, 22 outpatients) was recruited. Analyses included a comparison of CB&M and Berg Balance Scale (BBS) admission and change scores and associations between the CB&M and measures of postural sway, gait, and dynamic stability; the Community Integration Questionnaire (CIQ); and the Activities-specific Balance Confidence (ABC) Scale. RESULTS: Mean admission scores on the BBS and the CB&M were 53.6/56 (SD=4.3) and 57.8/96 (SD=23.3) respectively. Significant correlations were demonstrated between the CB&M and spatiotemporal measures of gait, including walking velocity, step length, step width, and step time; measures of dynamic stability, including variability in step length and step time; and the ABC (p<0.05). Significant correlations between the CB&M and CIQ were revealed with a larger data set (n=47 outpatients) combined from previous phases of research. CONCLUSIONS: In patients with TBI, the CB&M is less susceptible to a ceiling effect than the BBS. The construct validity of the CB&M was supported, demonstrating associations with laboratory measures of dynamic stability, measures of community integration, and balance confidence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".