Cognitive screening in substance users: Diagnostic accuracies of the Mini-Mental State Examination, Addenbrooke’s Cognitive Examination–Revised, and Montreal Cognitive Assessment
Bibliographic record
Abstract
INTRODUCTION: Despite the considerable prevalence of cognitive impairment in substance-using populations, there has been little investigation of the utility of cognitive screening measures within this context. In the present study the accuracy of three cognitive screening measures in this population was examined-the Mini-Mental State Examination (MMSE), the Addenbrooke's Cognitive Examination-Revised (ACE-R), and the Montreal Cognitive Assessment (MoCA). METHOD: A sample of 30 treatment-seeking substance users and 20 healthy individuals living in the community were administered the screening measures and a neuropsychological battery (NPB). Agreement of classification of cognitive impairment by the screening measures and NPB was examined. RESULTS: Results indicated that the ACE-R and MoCA had good discriminative ability in detection of cognitive impairment, with areas under the receiver-operating characteristic (ROC) curve of .85 (95% confidence interval, CI [.75. .94] and .84 (95% CI [.71, .93]) respectively. The MMSE had fair discriminative ability (.78, 95% CI [.65, .93]). The optimal cut-score for the ACE-R was 93 (impairment = score of 92 or less), at which it correctly classified 89% of individuals as cognitively impaired or intact, while the optimal cut-score for the MoCA was <26 or <27 depending on preference for either specificity or sensitivity. The optimal cut-score for the MMSE was <29; however, this had low sensitivity despite good specificity. CONCLUSIONS: These findings suggest that the MoCA and ACE-R are both valid and time-efficient screening tools to detect cognitive impairment in the context of substance use.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".