Determination of Mo<scp>CA</scp> Cutoff Score in Patients with Alcohol Use Disorders
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) score is a convenient and promising tool for estimating alcoholic patients' global cognitive functioning, a major challenge for all specialized alcohol treatment centers. However, whether or not the score should be corrected for education level and whether the proposed cutoff is relevant in patients with alcohol use disorders (AUD) should be determined. METHODS: We compared the MoCA scores in patients hospitalized for AUD with and without cognitive impairment assessed by a battery of neuropsychological (NP) tests. Sensitivity, specificity, and cutoff of the MoCA score were analyzed using receiver operating characteristic curve analysis. RESULTS: Thirty-one patients with and 25 without cognitive impairment were included in the study. There were 40 men and 16 women, with a mean age of 49.5 years. The mean uncorrected MoCA score was 23.1 ± 3.3 in those with and 27.0 ± 1.9 in those without cognitive impairment. NP tests were significantly correlated with the MoCA score. Uncorrected MoCA scores identified more than 80% of the patients with a cutoff score equal to 26, to obtain similar accuracy with the corrected score required using a cutoff score equal to 27. CONCLUSIONS: Our results confirm that the MoCA test is a convenient and reliable screening tool to measure cognition defects in alcoholic patients. As using the 1-point education adjustment increases the cutoff score by 1 point, it is suggested to use the noncorrected score and the usual cutoff, that is, 26. Being easy to administer and only moderately time-consuming, the MoCA score should be used extensively in addiction treatment centers.
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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 | 0.003 |
| 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".