P4–361: The influence of mild cognitive impairment on measures of fall risk in community‐dwelling older adults
Bibliographic record
Abstract
Fall related injuries are the fifth leading cause of death in older adults and result in medical costs of more than $20 billion per year. Cognitive impairment is a risk for falls and those with mild cognitive impairment (MCI) have changes in gait and are at a greater risk for falling. The purpose of this study was to determine how the presence of MCI as measured with the Montreal Cognitive Assessment tool (MoCA) influences performance on fall risk screening tools: the Five Times Sit to Stand test (FTSTS), usual gait speed, the Timed Up and Go test (TUG) and the Activities Specific Balance Confidence scale (ABC) measures. Forty seven community dwelling older adults participated. The MoCA was used to stratify the subjects into those with MCI and those without MCI based on a cutoff score of 26. Descriptive statistics and mean scores on each of the fall risk screening tools were reported. Correlations and linear regression analyses were completed. Mean scores on the fall risk tools did not differ between groups. Significant associations between each of the fall risk screening tools were found in those with MCI with the exception of the ABC and gait speed measurements. Regression models were able to predict the TUG and ABC in those with MCI while controlling for age, having a history of falling and taking greater than 4 medications The influence of deficits in cognitive processes reported in those with MCI may influence the interpretation of fall risk measures and the association of those cognitive deficits on fall risk should be considered. Although there were no significant differences detected in overall mean scores, these findings may provide evidence to the subtle influence that early cognitive loss as found in MCI has over overall performance on measures of fall risk. Further studies are needed to examine this influence.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".