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Record W7117276144 · doi:10.1002/alz70858_099108

The Cause and Consequence Relationships between Domestic Abuse and Alzheimer's Disease: Identification of Five Subtypes

2025· article· en· W7117276144 on OpenAlexaff
Emma Twiss, Carley McPherson, Donald F. Weaver

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsLakeridge HealthUniversity of TorontoKrembil Foundation
Fundersnot available
KeywordsDomestic violenceDiseaseDementiaIdentification (biology)Risk factorAffect (linguistics)Sexual abuseIncidence (geometry)

Abstract

fetched live from OpenAlex

Domestic abuse (DA) and Alzheimer's Disease (AD; and related dementias) are two of humankind's most significant societal healthcare issues: DA is widespread, with one in three women and one in four men experiencing physical, psychological, emotional, sexual and/or financial abuse in their lifetime; AD is the most common form of dementia and will affect more than 152 million people worldwide by 2050. Given the incidence and prevalence of these two problems, any cause or effect relationship between them carries profound societal consequences. Herein, we describe five types of overlapping relationships between DA and AD: 1. Intimate partner violence as a risk factor for AD; 2. AD as a risk factor for ongoing or worsening DA; 3. Abuse of caregivers by people with AD; 4. Abuse of people with AD by caregivers; and 5. Reactivation of DA behavior in a person with AD. Chronologically, these five types cover the spectrum from occurring decades before the start of AD symptoms to onset only after the AD symptoms have manifested. Mechanistically, these five subtypes reflect the paradoxical fact that DA and AD may be the cause or consequence (or both) of each other. Phenomenologically, they encompass the full spectrum of DA including physical, psychological, emotional, sexual and financial abuse. Given the challenges in recognizing, managing and treating both DA and AD, society's need to identify and prevent these five subtypes of DA/AD overlap is an emerging priority.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.348
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.326
Teacher spread0.286 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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