Consumo de alcohol y deterioro cognitivo de atención y memoria en trabajadores de la construcción
Bibliographic record
Abstract
Objective: To describe the levels of cognitive impairment in attention and memory in construction workers with alcohol consumption, residents of a town in southeastern Mexico. Material and Methods: The research had a quantitative, cross-sectional and descriptive approach. The sample was selected by convenience. The Alcohol Use Disorders Identification Test [AUDIT] was used to determine the level of alcohol consumption and the Montreal Cognitive Assessment Test [MoCA] was used to identify cognitive impairment of attention and memory. Results: according to the results, 43% of the participants showed a level of alcohol dependence. With respect to the cognitive functions assessed, it was determined that 53% of the workers assessed were globally at a level of mild cognitive impairment. When evaluating attention, 50% reached the normal level and 50% presented mild cognitive impairment. For memory, 43% of the participants were at the level of moderate impairment or probable dementia. Conclusions: the prevalence rates in the levels of memory impairment in the population with alcohol consumption studied are significant, aggravating the situation in construction employees where this function is essential to perform daily tasks that if not performed properly put the lives of all employees at risk. Keywords: Alcoholic beverage consumption, Cognition; Memory; Workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".