Use of the MoCA Combined with the Fab for the Screening of Cognitive Dysfunction in Patients with Alcohol Use Disorders
Bibliographic record
Abstract
Objective: Cognitive dysfunction is common in patients with Alcohol Use Disorders (AUD). This impairment needs to be detected since it affects the quality of life of patients and compliance with therapeutic programs. As global cognitive and executive functions may be differently affected in AUD patients, we wondered whether, when diagnosing cognitive dysfunction, specific measurement of executive functioning could provide an incremental value that could be used in addition to global cognitive measurement. Methods: Cognitive status was evaluated at admission using the Montreal Cognitive Assessment (MoCA) test, the Frontal Assessment Battery (FAB) and a battery of Neuropsychological (NP) reference tests in 134 patients with AUD hospitalized in an addictions treatment unit. Results: Seventy patients (52%) had cognitive dysfunction according to the battery of Neuropsychological (NP) tests. Among these 70 patients, 59 (84%) and 38 (54%) had abnormal MoCA and FAB test results, respectively. Concordance between the MoCA and the FAB was weak (kappa = 0.27). Analysis through logistic regression showed that the Area under Curve (AUC) obtained with the MoCA test was a better single predictor of cognitive impairment (0.85) than that obtained with the FAB (0.73). Combining the two tests produced an AUC of 0.86, a value not significantly different from that obtained with the MoCA. Conclusions: The MoCA-FAB combination did not perform better than the MoCA alone as a screening tool for cognitive dysfunction among AUD patients. This confirms that the MoCA is an efficient screening tool since it can detect frontal as well as general cognitive disorders.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".