Impaired or not impaired: The accuracy of the Montreal Cognitive Assessment in detecting cognitive impairment among patients with alcohol use disorder
Bibliographic record
Abstract
BACKGROUND: Cognitive impairments are common in alcohol use disorder (AUD), but only a few studies have investigated the accuracy of the Montreal Cognitive Assessment (MoCA) in this population. We examined the accuracy and precision of the MoCA in detecting cognitive impairment in a sample of patients with AUD. In addition, we investigated whether the MoCA predicts premature discontinuation from treatment. METHOD: A sample of 126 persons with AUD undergoing treatment in specialist health services were administered the MoCA and a battery of 12 neuropsychological tests. Five cognitive domains were derived from the reference tests. A composite total score from these tests was used as a reference criterion for determining correct and incorrect classifications for the MoCA. We analyzed the optimal cut-off score for the MoCA and the accuracy and agreement of classification between the MoCA and the reference tests. RESULTS: Receiver operating characteristic (ROC) curve analyzes yielded an area under the curve (AUC) of 0.77 (95% CI [0.67, 0.87]). Applying 25 as the cut-off, MoCA sensitivity was 0.77 and specificity 0.62. The PPV was 0.53. The NPV was 0.84. Using a cut-off score of 24 yielded a lower sensitivity 0.60, but specificity was significantly better i.e., 0.79. PPV was 0.68. The NPV was 0.82. Kappa agreement between MoCA and the reference tests was fair to moderate, 0.38 for the cut-off of 25, and 0.44 for the cut-off of 24. MoCA did not predict discontinuation from treatment. CONCLUSIONS: Our findings indicate limitations in the classification accuracy of the MoCA in predicting cognitive impairment in AUD. Achieving the right balance between accurately identifying impaired cases without including too many false positives can be challenging. Further, MoCA does not predict discontinuation from treatment. Overall, the results do not support MoCA as a time-efficient screening instrument.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".