Use of the Berg Balance Scale for Predicting Multiple Falls in Community-Dwelling Elderly People: A Prospective Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Falls are a significant public health concern for older adults; early identification of people at high risk for falling facilitates the provision of rehabilitation treatment to reduce future fall risk. The objective of this prospective cohort study was to examine the predictive validity of the Berg Balance Scale (BBS) for 3 types of outcomes-any fall (> or =1 fall), multiple falls (> or =2 falls), and injurious falls-by use of sensitivity, specificity, receiver operating characteristic (ROC) curves, area under the curve, and likelihood ratios. SUBJECTS AND METHODS: A sample of 210 community-dwelling older adults received a comprehensive geriatric assessment at baseline, which included the BBS to measure balance. Data on prospective falls were collected monthly for a year. The predictive validity of the BBS for the identification of future fall risk was evaluated. RESULTS: The BBS had good discriminative ability to predict multiple falls when ROC analysis was used. However, the use of the BBS as a dichotomous scale, with a threshold of < or =45, was inadequate for the identification of the majority of people at risk for falling in the future, with sensitivities of 25% and 45% for any fall and for multiple falls, respectively. The use of likelihood ratios, maintaining the BBS as a multilevel scale, demonstrated a gradient of risk across scores, with fall risk increasing as scores decreased. DISCUSSION AND CONCLUSION: The use of the BBS as a dichotomous scale to identify people at high risk for falling should be discouraged because it fails to identify the majority of such people. The predictive validity of this scale for multiple falls is superior to that for other types of falls, and the use of likelihood ratios preserves the gradient of risk across the whole range of scores.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".