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Record W4390193523 · doi:10.1002/alz.074992

Sex Differences in Behavioral and Psychological Symptoms in Institutionalized Patients with Advanced Alzheimer’s Disease

2023· article· en· W4390193523 on OpenAlexaff
Samira Choudhury, Sarah Colman, Li Chu, Simon Davies, Peter Derkach, Sarah Elmi, Corinne E. Fischer, Philip Gerretsen, Ariel Graff‐Guerrero, Maria Hussain, Zahinoor Ismail, Shehroz S. Khan, Donna Kim, Linda Krisman, Rola Moghabghab, Benoit H. Mulsant, Vasavan Nair, Bruce G. Pollock, Soham Rej, Aviva Rostas, David L. Streiner, Lisa Van Bussel, Tarek K. Rajji, Sanjeev Kumar, Amer M. Burhan

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsToronto Dementia Research AllianceMcMaster UniversityToronto Rehabilitation InstituteOntario Shores Centre for Mental Health SciencesHotchkiss Brain InstituteMcGill UniversityUniversity of CalgaryQueen's UniversitySt. Michael's HospitalWest Park Healthcare CentreCentre for Addiction and Mental HealthUniversity Health NetworkWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsDementiaIrritabilityPsychosisPsychologyPsychiatryAnxietyDistressDepression (economics)Clinical psychologyMedicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Behavioral and psychological symptoms in dementia (BPSD) are highly prevalent in patients with Alzheimer’s dementia (AD). We examined sex differences in the frequency and severity of BPSD in patients with advanced AD residing in long term care or admitted to inpatient psychiatric units. Also, sex differences in the frequency and severity of BPSD symptom clusters were explored. Method We analyzed data from the Standardizing Care for Neuropsychiatric Symptoms and Quality of Life in Dementia (StaN) study (ClinicalTrials.gov/NCT03672201). BPSD was assessed with the Neuropsychiatric Inventory Clinician rating scale. Chi‐squared tests and Mann‐Whitney U tests were used to determine sex differences in frequency and severity of BPSD, respectively. Bonferroni correction was used for multiple comparisons. Generalized linear models were performed to examine effect of sex on severity of BPSD while controlling for age and residence. BPSD symptom clusters were defined as: (1) psychosis (hallucinations and delusions), (2) emotional distress (depression and anxiety), and (3) agitation (agitation, aggression, irritability, aberrant motor behavior, and aberrant vocalizations). Result 194 participants (99 female, 95 male) were included. Females had higher frequency of delusions (X2(N = 176) = 8.47, p = .004) and males had higher frequency of sleep disturbances (X2(N = 176) = 9.89, p = .002). Further, females had greater severity of delusions (N = 176, U = 2976, p = .001), and sleep disturbances (N = 176, U = 2950, p = .002). In generalized linear models, sex was associated with severity of delusions (Wald X2 = 3.97, N = 176, p = .046), but not with sleep disturbances. Examination of clusters revealed that females had higher frequency of psychosis (X2(N = 176) = 4.25, p = .039). Conclusion We observed sex differences in the frequency and severity of specific BPSD. Future studies should aim to understand potential mechanisms underlying these differences, and to study their relevance for screening, and individualized management of BPSD.

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)0.002
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.356
Teacher spread0.300 · 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 routes1
Has abstractyes

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