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Record W4413021894 · doi:10.4088/jcp.24m15738

Delineating the Effects of Alcohol Use on Cognition in Individuals With Neurocognitive Disorders

2025· article· en· W4413021894 on OpenAlexaffabout
Ari B. Cuperfain, Sandra E. Black, Mira Fostoc, Morris Freedman, Clement Ma, Tarek K. Rajji, Stephen C. Strother, David F. Tang‐Wai, Maria Carmela Tartaglia, Sanjeev Kumar

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicAlcoholism and Thiamine Deficiency
Canadian institutionsPublic Health OntarioCentre for Addiction and Mental HealthBaycrest HospitalHealth Sciences CentreMount Sinai HospitalSunnybrook Health Science CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsNeurocognitiveCognitionPsychologyAlcoholClinical psychologyPsychiatryCognitive psychology

Abstract

fetched live from OpenAlex

Excessive alcohol use is a recognized modifiable risk factor for the development of dementia; however, the neuropsychological profile of cognitive impairment seen with alcohol use is heterogeneous. We studied cognitive characteristics associated with alcohol use in a "real-world" memory clinic cohort of patients with neurocognitive disorders. We used the Toronto Dementia Research Alliance memory clinic research database to generate an age, sex, and education matched sample of individuals with alcohol-related cognitive impairment (ARCI group; n=51) and twice as many individuals without such history (Comparator group; n=102). We compared cognitive domain and subdomain Toronto Cognitive Assessment scores between the two groups using linear regression.while controlling for age, sex, education, concurrent psychiatric disorders, global cognition, and traumatic brain injury. =.018). Our study suggests that ARCI results in specific deficits involving cognitive control during delayed recall task. This may help advance development of markers to delineate ARCI from other causes of cognitive impairment. Future work may test these findings in larger, well-characterized samples.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.012
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.388
Teacher spread0.355 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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