Screening Tools for Cognitive Impairment in Adults with Substance Use Disorders: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Objectives: Cognitive impairment is common in individuals with substance use disorders (SUDs), yet no evidence-based guidelines exist regarding the most appropriate screening measure for use in this population. This systematic review aimed to (1) describe different cognitive screening measures used in adults with SUDs, (2) identify substance use populations and contexts these tools are utilised in, (3) review diagnostic accuracy of these screening measures versus an accepted objective reference standard, and (4) evaluate methodology of included studies for risk of bias. Methods: Online databases (PsycINFO, MEDLINE, Embase, and CINAHL) were searched for relevant studies according to pre-determined criteria, and risk of bias and applicability was assessed using the Quality Assessment of Diagnostic Accuracy Studies–2 (QUADAS–2). At each review phase, dual screening, extraction, and quality ratings were performed. Results: Fourteen studies met inclusion, identifying 10 unique cognitive screening tools. The Montreal Cognitive Assessment (MoCA) was the most common, and two novel screening tools (Brief Evaluation of Alcohol-Related Neuropsychological Impairments [BEARNI] and Brief Executive Function Assessment Tool [BEAT]) were specifically developed for use within SUD populations. Twelve studies reported on classification accuracy and relevant psychometric parameters (e.g., sensitivity and specificity). While several tools yielded acceptable to outstanding classification accuracy, there was poor adherence to the Standards for Reporting Diagnostic Accuracy Studies (STARD) across all studies, with high or unclear risk of methodological bias. Conclusions: While some screening tools exhibit promise for use within SUD populations, further evaluation with stronger methodological design and reporting is required. Clinical recommendations and future directions for research are discussed.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".