Relation between Smoking and Cognition in Egyptian Elderly
Bibliographic record
Abstract
Background: In spite of numerous studies published in the past few years on the topic, the effect of smoking on Alzheimer's disease and dementia remains uncertain. Case–control studies have largely suggested that smoking lowers the risk of AD, whereas prospective studies have shown that smoking increases this risk or has no effect on the probability of developing AD. Objectives: The aim of this study is to compare the prevalence of Smoking in elderly with cognitive impairment and elderly with non-cognitive impairment. Design: A Case control study. Participants: 88 participants aged 60 years and above. They were selected from Ain Shams University Hospital from inpatient wards and outpatient clinics. The studied sample was divided into 3 groups: Group A (32 elderly patients with Alzheimer's disease), Group B (32 elderly patients with Mild cognitive impairment) and Group C (24 controls with normal cognitive function). Measurements: Comprehensive geriatric assessment, including detailed history, physical examination, and also cognitive assessment using Montreal Cognitive Assessment (MOCA) and Mini mental status examination (MMSE). Results:As regards smoking there was a highly statistical significant difference between the 3 groups as non-smokers were more prevalent in Alzheimer's disease and Mild cognitive impairment groups in comparison to control group with (p-value= 0.001). Conclusion: There was a highly significant negative association between smoking and cognitive impairment.
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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".