Alcohol-related cognitive impairment: neuropsychological findings and cognitive assessment in a clinical context
Bibliographic record
Abstract
Background: It is well established that chronic alcohol consumption can detrimentally impact brain structure and function. In the clinical setting, however, diagnosis of alcohol-related cognitive impairment (ARCI) is frequently complicated by the presence of comorbid psychiatric and health conditions. It is unclear which neuropsychological tasks best detect cognitive impairment in alcohol use disorder (AUD) samples in which these comorbid conditions are present. Aims: The research included in the current thesis was designed to provide clinically useful findings regarding the neuropsychological features of ARCI, including the presentation of ARCI in individuals with comorbid conditions. Method: A systematic review of the literature was conducted to examine the neuropsychological profile of two alcohol-related cognitive disorders: alcohol-related dementia and Korsakoff syndrome. In addition, two empirical studies were conducted. In the first, a neuropsychological battery was administered to 21 participants diagnosed with AUD and a control group matched on age, education and gender. Statistical comparisons between groups on cognitive tasks were performed. In the second, the diagnostic accuracy of the Mini Mental State Examination (MMSE), Addenbrooke's Cognitive Examination-Revised (ACE-R), and the Montreal Cognitive Assessment (MoCA) in detection of cognitive impairment was examined. This was evaluated in 30 individuals with substance use disorder diagnoses and 20 healthy controls using the receiver operating characteristic method. Results: The results of the systematic review demonstrated the heterogeneity in methodological approaches, which preclude definitive conclusions being drawn regarding the neuropsychological profile of alcohol-related cognitive syndromes. The results of the first empirical study confirmed the high rates of comorbid psychiatric, neurological and health conditions that accompany individuals with AUD. Participants in the AUD group were most frequently impaired in the delayed memory domain, while semantic fluency and visuospatial memory tasks best distinguished the AUD group from controls. In the second empirical study, it was demonstrated that the ACE-R and the MoCA had superior discriminative qualities to the MMSE in the detection of cognitive impairment in the substance use sample. It was concluded that further validation of cognitive tasks appropriate for assessment of the SUD population is necessary in future research.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".