Cognitive functioning and falls in older people: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To identify which cognitive functions and specific neuropsychological assessments predict falls in older people living in the community. METHODS: Five electronic databases were searched until 30/08/2022 for studies assessing the association between specific cognitive functions and faller status (prospective and retrospective), in community-dwelling older people. Risk of bias was assessed with the Newcastle-Ottawa Scale. Meta-analyses synthesised the evidence regarding the associations between different neurocognitive subdomains and faller status. RESULTS: Thirty-eight studies (20 retrospective, 18 prospective) involving 37,101 participants were included. All but one study was rated high or medium quality. Meta-analyses were performed with data from 28 studies across 11 neurocognitive subdomains and four specific neuropsychological tests. Poor cognitive flexibility, processing speed, free recall, working memory and sustained attention were significantly associated with faller status, but poor verbal fluency, visual perception, recognition memory, visuo-constructional reasoning and language were not. The Trail Making Test B was found to have the strongest association with faller status. CONCLUSION: Poor performance in neurocognitive subdomains spanning processing speed, attention, executive function and aspects of memory are associated with falls in older people, albeit with small effect sizes. The Trail Making Test, a free-to-use, simple assessment of processing speed and mental flexibility, is recommended as the cognitive screening test for fall risk in older people.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".