Assessing the impact of falls on neuropsychiatric symptoms in patients with neurodegenerative disease
Bibliographic record
Abstract
Abstract Background Falls are the most common injury faced by older adults and those with neurodegenerative diseases. Falls can result in concussion/mild traumatic brain injury(mTBI). Concussions in older adults or those with neurodegenerative disease can have a significant impact on behavior as post‐concussion symptoms include neuropsychiatric issues. We hypothesized that there is a relationship between past fall and neuropsychiatric symptoms and neuropsychiatric symptom severity. Methods We used data on falls and Neuropsychiatric Inventory (NPI) from the Ontario Neurodegenerative Disease Research Initiative dataset for 480 individuals with neurodegenerative diseases (Alzheimer’s Disease, Parkinson’s Disease, Amyotrophic lateral sclerosis, frontotemporal dementia and vascular cognitive impairment). We used the Chi‐squared and Mann‐Whitney tests to compare frequency of NPI symptoms (anxiety, depression, irritability, disinhibition, apathy, delusions, hallucinations, agitation, euphoria, motor‐disturbance, night‐time behaviour, appetite), and total NPI severity and distress, respectively, between patients with and without falls in the past 12 months. Results Comparing patients with falls (n = 169; mean‐age = 68.3±9; 36% F) to patients without falls (n = 311; mean‐age = 68.7±7; 32% F), there was a significantly higher frequency of anxiety (Chi‐squared test, X2 (df = 1, N = 480) = 12.859, P‐value = 0.0003); higher median anxiety severity (Mann‐Whitney/Wilcoxon‐test p‐value = 0.0002); and higher median partner anxiety distress (Wilcox test p‐value = 0.0006) in those who had had a previous fall compared to those who had not, even with multiple comparison correction. Depression, apathy, disinhibition, night‐time behaviours, and eating/appetite changes and total NPI severity were significantly worse in those with previous falls but did not survive multiple comparison correction. Conclusion We found that anxiety frequency, severity and distress were much higher in patients with neurodegenerative disease who had a fall in the preceding 12 months compared to those without falls. Our study suggests that neuropsychiatric symptoms, especially anxiety are frequent and should be assessed in those with previous falls as they can be a consequence of mild brain injury and may contribute to worsening cognition or behaviors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".