Relationship Between Smoking Status and Cognitive Function in a Group of Older Egyptians
Bibliographic record
Abstract
Background: Current evidence regarding the complex relationship between cigarette smoking and accelerated cognitive decline remains inconsistent. Clarifying this critical link is essential for identifying modifiable lifestyle risk factors to inform prevention strategies for Mild Cognitive Impairment (MCI) and subsequent dementia development. Methods: This cross-sectional study was conducted at the outpatient clinics of Helwan University Hospital. A total of 120 elderly participants (≥ 60 years) of both sexes were enrolled in the study. Smoking history, sociodemographic characteristics, cognitive function assessment using the Montreal Cognitive Assessment (MoCA), and Activities of Daily Living (ADL) and Instrumental ADL (IADL) were assessed for all participants. Results: Of the 120 participants, 27 were smokers. The prevalence of MCI did not significantly differ between smokers and non-smokers (63.0% vs. 46.2%, P=0.162). Mean MoCA (24.3 vs. 24.8, P=0.434) score was comparable across groups, with no correlation with smoking duration or smoking intensity. However, smokers required more assistance with ADL (22.2% vs. 6.5%, P=0.016). After adjusting for both sex and age, ex-smokers demonstrated significantly lower MoCA scores compared to non-smokers (β=−7.27, P=0.016), while current smokers showed a trend towards lower scores. Conclusion: While unadjusted comparisons suggested comparable cognitive function, multivariable analysis revealed that a history of smoking, particularly in ex-smokers, was associated with lower cognitive performance and reduced functional independence in older Egyptians adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".