Cognitive decline and alcohol consumption adjusting for functional status over a 3-year period in French speaking community living older adults
Bibliographic record
Abstract
BACKGROUND: The effect of alcohol consumption on cognitive decline is not clear. We aimed to study the association between alcohol consumption and cognitive functioning controlling for functional heath status. METHODS: A total of 1610 older adults with a score ≥26 on the Mini-Mental State Examination (MMSE) were followed to assess the change in scores at the 3-year follow-up. Information on alcohol consumption as well as socio-demographic, lifestyle, psychosocial and clinical factors, as well as health service use were assessed at baseline and 3-year follow-up interviews. Linear mixed models with repeated measures were used stratifying by functional status. RESULTS: Close to 73% reported consuming alcohol in the past 6 months, of which 11% were heavy drinkers (≥11 and ≥16 drinks for women and men). A significant decrease in MMSE scores was observed in low functioning non-drinkers (-1.48; 95% CI: -2.06, -0.89) and light to moderate drinkers (-0.99; 95% CI: -1.54, -0.44) and high functioning non-drinkers (-0.51; 95% CI: -0.91, -0.10). CONCLUSIONS: Alcohol consumption did not contribute to cognitive decline. Cognitive decline was greater in individuals reporting low functional status. Research should focus on the interaction between changing patterns of alcohol consumption and social participation in individuals with low and high functioning status.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".