39 A Model of Gait and Falls in Older Adults with Dementia
Bibliographic record
Abstract
Abstract Background Older people with cognitive impairment are at increased risk of falls; however, fall prevention strategies have limited success in reducing fall risks in this population (Fernando E, Fraser M, Hendriksen J et al. Physiotherapy Canada. 2017; 69: 161–170). We aim to present a model of factors contributing to gait and falls in older adults with dementia. Methods The model was developed based on an in-depth review of literature on fall risk factors particularly in people with dementia, and the relationship between cognition and gait, and their joint impact on risk of falls. Results Cognitive and motor functions are closely related as they share neuroanatomy (Rosso AL, Studenski SA, Chen WG et al. J Gerontol A Biol Sci Med Sci. 2013; 68: 1379–1386). This close relationship has been confirmed by imaging, observational and interventional studies. Executive function is the cognitive domain most commonly associated with gait dysfunction (Cohen JA, Verghese J, Zwerling JL. Maturitas. 2016; 93: 73-77). The sub-domains of executive function(Sachdev PS, Blacker D, Blazer DG et al. Neurology. 2014; 10: 634-642) - attention, sensory integration and motor planning affect risk of falls through gait dysfunction; whereas other non-gait associated sub-domains of executive function - cognitive flexibility, judgement and inhibitory control affect risk of falls through risk taking behaviour. Conclusion Gait, cognition and falls are closely related. The comoridity and interaction between gait abnormality and cognitive impairment may be the underlying mechanism behind the high prevalence of falls in older adults with dementia. Gait and cognitive assessment with particular focus on executive function, should be integrated in fall risk screening. Assessment results should inform interventions developed by a multidisciplinary team and may include strategies such as customised gait training and behavioural modulation. A comprehensive multidisciplinary approach could be more effective in reducing fall risks in older adults with dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".