Relevance of the body mass index in the cognitive status of diabetic patients with different alcohol-drinking patterns
Bibliographic record
Abstract
Nowadays the general relevance of alcohol consumption in diabetes is extremely controversial. There are recent reports that alcohol consumption could result in a decreased incidence of diabetes, as well as other studies demonstrating a positive association between alcohol consumption and type 2 diabetes; there are also reports arguing for an inverse association between the two or for no correlation at all. The different results obtained in these studies could be explained by the existence of several confounders that could influence the outcome of the aforementioned studies. In this paper, we studied the possible relevance of BMI as a confounder in the relationship between alcohol consumption in diabetes and cognitive function, by analyzing the correlations between BMI values in diabetic patients with different alcohol drinking patterns and the subdomains from some main psychometric tests, such as MMSE (Mini-Mental State Examination) and MOCA (Montreal Cognitive Assessment). Our results provide evidence for BMI as a possible confounder of the relationship between alcohol consumption in diabetes and cognitive function. We found a significant increase (p<0.0001) in BMI values in patients with diabetes compared to our control group. Most importantly, significant correlations between BMI parameters in alcohol-consuming diabetic patients and most of the subdomains for psychometric testing.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".