Alcohol Consumption and Subclinical Findings on Cognitive Function, Biochemical Indexes, and Cortical Anatomy in Cognitively Normal Aging Han Chinese Population
Bibliographic record
Abstract
Background: Binge drinking of alcohol is associated with brain damage, but less is known about relationship of light-to-moderate alcohol consumption with cognitive function, biochemical indexes, and cortical anatomy. Previous findings indicate debate on whether light-to-moderate drinking has any health benefits. We investigated cortical thickness and its association with alcohol consumption and cognitive functions in a non-dementia aging Han Chinese population. Methods: 940 non-dementia aging subjects were included in our study (alcohol n=149; non-alcohol n=791). Among them, 572 received blood biochemical tests including liver function and lipid metabolism (alcohol n=100; non-alcohol n=472) and 141 had brain magnetic resonance imaging (alcohol n=27; non-alcohol n=114). The Beijing version of the Montreal Cognitive Assessment (MoCA) and the Chinese version of the neuropsychological test battery (NTB) were used to assess cognitive functions. Results: There was no significant difference in cognitive functions between alcohol and non-alcohol groups in the overall database. Similarly, there was no significant difference in liver function and lipid metabolism between two groups in the sub-database. The left superiortemporal gyrus was one of age sensitive regions and alcohol consumption was significantly associated with thinner cortex of the left superiotemporal cluster in the sub-database. Conclusion: Alcohol consumption was not significantly associated with better or worse cognitive function and biochemical indexes abnormality, however, significantly associated with thinner cortex of the left superiortemporal gyrus in cognitively normal aging Han Chinese population.
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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 | 0.000 | 0.001 |
| 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".