Prevalence of cognitive impairment in patients with substance use disorder
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Cognitive impairments in substance use disorder predict treatment outcome and are assumed to differ between substances. They often go undetected, thus the current study focuses on the prevalence of and differences in cognitive functioning across substances by means of a cognitive screen at the early stage of addiction treatment. DESIGN AND METHODS: The Montreal Cognitive Assessment was administered to outpatients seeking treatment for substance use disorder. Patient characteristics (age, years of regular use, polysubstance use, severity of dependence/abuse, depression, anxiety and stress) were also taken into account. RESULTS: A total of 656 patients were included (n = 391 used alcohol, n = 123 used cannabis, n = 100 used stimulants and n = 26 used opioids). The prevalence of cognitive impairments was 31%. Patients using alcohol had a lower total- and memory domain score than those using cannabis. Patients using opioids scored lower on visuospatial abilities than those using cannabis or stimulants. Younger patients scored higher than older patients. No effect was found for the other investigated characteristics. DISCUSSION AND CONCLUSIONS: Given the high prevalence of cognitive impairments, standard screening at an early stage of treatment is important to determine the course of treatment and maximise treatment outcome. Caution is needed in interpreting results about opioids due to an underrepresentation of this patient group, and more research is needed on the effect of age on Montreal Cognitive Assessment performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".