Examining the Effects of Excessive Alcohol Use on Verbal Memory Processes in Older Adults with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Excessive alcohol use (EAU) is associated with cognitive impairment. Previously we have shown that older adults with EAU have higher rates of intrusions during verbal memory recall. Here, we further characterize the effects of alcohol on verbal memory processes by examining different types of intrusions during recall. METHOD: Two multicenter cohorts, the Comprehensive Assessment of Neurodegeneration and Dementia and the Ontario Neurodegenerative Disease Research Initiative, provided data for this study. Participants were diagnosed with MCI due to Alzheimer's disease (AD-MCI) or cerebro-vascular disease (V-MCI) per established criteria, and categorized based on their current and past alcohol use into 'zero', 'low-medium' (less than 1 to 7 standard drinks/week), or 'high' (>7 standard drinks/week) alcohol use groups. We coded the intrusions on the English version of the Rey Auditory Verbal Learning Test (RAVLT) as those with or without interference, i.e. before or after presentation of list B during list A recall. We calculated the change in intrusions with and without interference and used independent t-tests to compare this change between high/zero, high/low-medium, and low-medium/zero alcohol groups. RESULT: We included 338 participants with mean (SD) age = 71.62 (7.58) years. The zero alcohol group included 120 (females = 50.0%; V-MCI = 55.8%), the low-medium alcohol group 153 (females = 40.5%; V-MCI = 47.7%), and the high alcohol group 65 (females = 21.5%; V-MCI = 50.8%) participants. There were no differences in total or specific intrusion types between the groups. Participants in the high alcohol group exhibited greater increase in intrusions, mean (SD) = 1.71 (2.19), versus 1.01 (2.65) in the low-medium group, and 0.86 (2.23) in the zero group following exposure to the interference list. Differences were significant for comparisons between high/zero (p = 0.014) and high/low-medium groups (p = 0.047), but not between low-medium/zero groups (p = 0.60). CONCLUSION: Participants with MCI and EAU are more susceptible to interference from recently learned task-irrelevant information during recall, suggestive of impaired cognitive control. These findings may inform future research regarding cognitive profiles and cognitive training interventions for older adults with MCI with EAU.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".