ASSOCIATION BETWEEN COGNITION AND FALL RISK BASED ON THE STEADI ALGORITHM: PROJECT VIBE
Bibliographic record
Abstract
Abstract Falls are a growing concern among older adults with estimates that one in four fall each year. Older adults who experience a fall are at higher risk for poor health outcomes that threaten independence and increase risk of death. Impairment in cognitive function is known to be associated with greater fall occurrence; however, cognitive testing is not an integral part of clinical fall risk assessment. The purpose of this study is to examine cognitive performance in relation to fall risk level and its components determined using the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) algorithm. One hundred eight community dwelling older adults (mean age 79(SD 7.3) years, 76% women, and 56% college or higher education) were included. Cognition was assessed with the Montreal Cognitive Assessment (MoCA; >= 26 normal). The STEADI algorithm was used to assess fall risk (low vs. moderate/high) based on the Stay Independent screening (>= 4), impairment in gait (Timed Up and Go (TUG)), strength (30-second chair stand), and balance (4-stage balance), and number of falls (>= 2). Associations between cognition and fall risk and its components were assessed using logistic regression adjusting for age, gender, and education. Normal cognitive status was marginally associated with lower likelihood of moderate/high compared to low fall risk (OR 0.42, 95% CI 0.17-1.04), and with a lower likelihood of TUG impairment (OR 0.22, 95% CI 0.07-0.66). These results suggest cognitive status may contribute important information about older adults’ fall risk and should be considered an integral part of fall risk assessment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".