Do Overweight, Arterial Hypertension and Type 2 Diabetes Worsen Cognitive Impairment in Patients with Alcohol Use Disorders?
Bibliographic record
Abstract
Objective: Overweight, Arterial Hypertension (AH) and diabetes are frequently associated with alcohol use disorders. As each of these co-morbidities is independently associated with cognitive impairment, we studied whetherthey could worsen alcohol-related cognitive impairment. Methods: A retrospective analysis of a clinical database of patients with an alcohol use disorder admitted to an addiction treatment unit of a teaching hospital. Patient weight was classified using WHO recommendations; arterial hypertension and Type 2 diabetes were diagnosed according to the most recent guidelines. Cognitive status was assessed using the MoCA administered on admission and at discharge by trained staff members. Results: Among the 387 patients included (69.3% male, mean age 50.4), 6.4% suffered from Type II diabetes, AH was present in 22.4% of the sample, and 20.6% were obese (BMI>=30). MoCA scores at admission did not differ as a function of BMI, or AH or Type II diabetes status. At discharge, MoCA scoreshad improved in all subgroups; however, a multivariate analysis showed that they had improved significantly less in the AH group compared to the non-AH group. Conclusions: Our results confirm the impact of hypertension on cognitive dysfunction, including in patients with severe alcohol use disorders. Monitoring of blood pressure levels is, therefore, an important preventive measure for cognitive dysfunction in these patients.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".