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Record W4390201301 · doi:10.1002/alz.077388

Assessing the impact of falls on neuropsychiatric symptoms in patients with neurodegenerative disease

2023· article· en· W4390201301 on OpenAlexaffabout
Goldin Joghataie, Allison A. Dilliott, Andrew Frank, Anthony E. Lang, Angela Roberts, Angela K. Troyer, Brian Levine, Stephen R. Arnott, Brian Tan, Corinne E. Fischer, Connie Marras, Donna Kwan, Douglas P. Munoz, David F. Tang‐Wai, Elizabeth Finger, Ekaterina Rogaeva, J. B. Orange, Joel Ramirez, Kelly M. Sunderland, Lorne Zinman, Malcolm A. Binns, Michael Borrie, Mario Masellis, Morris Freedman, Manuel Montero‐Odasso, Miracle Ozzoude, Robert Bartha, Richard H. Swartz, Agessandro Abrahão, Bill McIlroy, Michael J. Strong, Maria Carmela Tartaglia

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsRobarts Clinical TrialsLawson Health Research InstituteHeart and Stroke FoundationUniversity of WaterlooParkwood InstituteToronto Dementia Research AllianceCentre for Addiction and Mental HealthSunnybrook HospitalQueen's UniversitySt. Michael's HospitalSunnybrook Health Science CentreOntario Brain InstituteParkinson's Clinic of Eastern Toronto & Movement Disorders CentreBaycrest HospitalUniversity of OttawaUniversity of TorontoWestern UniversityUniversity Health NetworkHealth Sciences CentreOccupational Cancer Research CentreToronto Western Hospital
Fundersnot available
KeywordsApathyIrritabilityAnxietyDepression (economics)MedicinePsychiatryDisinhibitionDementiaBeck Anxiety InventoryConcussionPsychologyDiseasePoison controlInternal medicineBeck Depression InventoryInjury preventionCognition

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.302
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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