Mo<scp>CA</scp> as a Screening Tool of Neuropsychological Deficits in Alcohol‐Dependent Patients
Bibliographic record
Abstract
BACKGROUND: Alcoholism is known to be associated with cognitive deficits mainly concerning visuospatial capacity, executive function, memory, and attention. These impairments may affect treatment efficacy which should therefore be adapted. We evaluated the potential utility of the Montreal Cognitive Assessment (MoCA) to evaluate cognitive impairment in a large series of alcoholic patients hospitalized for withdrawal and rehabilitation. METHODS: Consecutive recruitment during a time period of patients admitted to an addiction treatment unit of a teaching hospital. Administration of the MoCA test on admission by trained staff members. RESULTS: A total of 166 patients aged 49.9 ± 9.2 years were included. Mean duration of administration was 20 minutes. The mean MoCA score was 23.5 ± 3.5 and 68.1% had an impaired value (<26). Age was negatively and education was positively associated with the MoCA score. Significant cognitive deficits concerned visuospatial capacity, attention, fluency, abstraction, and delayed recall. Neither age nor sex was significantly related to the MoCA score, while having a high education level (>12 years) significantly increased the likelihood of having a high MoCA score. CONCLUSIONS: Owing to their severity and frequency, screening for cognitive deficits is necessary in alcoholics during rehabilitation. MoCA is an appropriate tool for this purpose.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".