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Record W4414718819 · doi:10.1080/14659891.2025.2566650

Prevalence of cognitive impairment in Moroccan patients with substance use disorder

2025· article· en· W4414718819 on OpenAlexaboutno aff
Salma Ait Bouighoulidne, Noura Dahbi, Amina Aquil, Oussama Raouani, Maroua Guerroumi, Ouassil El Kherchi, Arumugam R. Jayakumar, Fatima Zahra Laamırı, Abdeljalil Elgot

Bibliographic record

VenueJournal of Substance Use · 2025
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentSubstance useCognitionSubstance abusePrevalence

Abstract

fetched live from OpenAlex

Background Alcohol and cannabis use disorders are often linked to cognitive impairments, particularly in memory, attention, and executive functions. This cross-sectional study examined the prevalence and cognitive profiles of individuals with alcohol and/or cannabis use disorders attending addiction treatment centers in Morocco.Methods From January 2023 to January 2024, participants were assessed using a structured questionnaire covering sociodemographic data and cognitive screening via the Montreal Cognitive Assessment (MoCA), including the Memory Index Score (MIS) and Attention Index Score (AIS).Results Median MoCA scores were 19 for cannabis users, 20 for alcohol users, and 19 for individuals with both disorders. Cognitive impairment severity differed significantly across groups (p < 0.05), with severe deficits more common in those with co-occurring use, and moderate to mild impairments more frequent in cannabis users. MIS and AIS did not vary significantly between groups. Cognitive performance was significantly associated with education level (p < 0.001) and place of residence (p < 0.05).Conclusion The study highlights a high prevalence of cognitive impairment among individuals with substance use disorders in Morocco, supporting the need for routine cognitive screening in addiction treatment programs.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.029
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.289
Teacher spread0.258 · 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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