Assessment of cognitive function in patients with alcohol dependence: A cross-sectional study
Bibliographic record
Abstract
Background: This study aimed to evaluate cognition in patients with alcohol dependence. During the past decade, there has been an increasing interest in the evaluation of cognitive function in substance use disorders. Substance use includes the use of licit substance such as alcohol, tobacco, and diversion of drugs as well as illicit substances. Alcohol in beverage form is among the most widely used psychoactive drugs in the world, and it has dependence-producing properties. Ethanol in alcohol is a chemical and after consumption has a multitude of effects. Aims and Objectives: The aim of this study was to assess the cognitive functions in patients with alcohol dependence as compared to the normal controls using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Materials and Methods: This study included 44 patients with alcohol dependence diagnosed as per international classification of disease tenth edition criteria with a mean age of 43.61 ± 7.38. Cognition was tested using a sensitive battery of psychometric testing MMSE and MoCA. Results: Compared with healthy controls (n = 44), patients had lower total scores of cognitive testing MMSE (P = 0.010) and MoCA (P = 0.000). Conclusion: Our results indicted cognitive impairment in patients with alcohol dependence. This is important to determine prognosis and managing patients. [Natl J Physiol Pharm Pharmacol 2018; 8(3.000): 337-340]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".