Falls in older adults and patients with neurodegenerative disease: gaps in clinical practice and associations with cognitive and neuropsychiatric symptoms
Bibliographic record
Abstract
The incidence of falls is significantly higher and recovery from fall-related complications are significantly worse in those with neurodegenerative disease compared to otherwise healthy older adults. Cognitive dysfunction and neuropsychiatric symptoms (NPS) can be both fall risk factors and connected to fall-related complications. However, it is unclear if and how individuals with previous falls differ in their performance on cognitive tasks and NPS evaluations compared to those without falls. In our first study, we assessed physician practice towards older adults and those with neurodegenerative disease regarding falls and post-fall concussions. We found a critical gap as most physicians do not inquire about falls nor about post-concussion symptoms even if a fall is reported. In our second study, evaluating 483 individuals across five different neurodegenerative disease cohorts, we aimed to evaluate whether there were differences in cognitive function and NPS between those with a fall history and those without a history of falls. Compared to those without a fall history, individuals with neurodegenerative disease with a history of falls performed significantly worse in attention and working memory, and executive function; and had worse NPS severity, specifically for anxiety and night-time behaviours. Our results underscore that a greater attention is needed towards falls in those with neurodegenerative diseases.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".