Cognitive Function among People with Severe Substance Use
Bibliographic record
Abstract
INTRODUCTION: Studies report a high variability of cognitive impairment in people who use drugs, ranging from 20% to 80%. Most research focuses on individuals who use drugs who are either admitted to treatment facilities or incarcerated and being abstinent from substances. The present study aimed to assess cognitive function among populations with ongoing, severe, habitual substance use, mimicking a real-world day-to-day situation. METHODS: Cross-sectional design with 171 participants (70.2% male) with severe substance use, recruited from two sites in Oslo, Norway. All participants were screened for cognitive function using the Montreal Cognitive Assessment (MoCA). A cutoff of <26 points was used to classify possible cognitive impairment. Participants also provided information on their alcohol and substance use, as well as demographic data. RESULTS: 74.9% of the participants scored below the MoCA cutoff for possible cognitive impairment. We did not find any associations between scoring below the MoCA cutoff <26 and the substance use variables (substance use, number of substances used, history of overdoses, injection drug use, and past substance use treatment). CONCLUSION: A high proportion of people with severe substance use may experience a functional cognitive impairment. This study provides novel insights into cognitive function within a population actively engaged in habitual substance use, offering a real-world perspective with high external validity. This knowledge is highly relevant for service providers who aim to deliver tailored follow-up services to this population outside of traditional treatment settings.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".