Prevalence and Risk Factors of Cognitive Impairment and its Effect on Quality of Life
Bibliographic record
Abstract
Objectives: Examine the prevalence of cognitive impairment within Wave 1 of the Irish Longitudinal Study on Aging (TILDA) cohort and its relationship with comorbidities and lifestyle factors. The effect of cognitive impairment on quality-of-life scores was also investigated. Methods: A secondary cross-sectional analysis of data from Wave 1 of the TILDA cohort was undertaken. Results: Prevalence of cognitive impairment ranged between 5.8% and 51.2%, depending on the instrument used (Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA], respectively). Having hypertension (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.36–2.08), being a past or current smoker (OR 1.25; 95% CI 1.01–1.55) and having low physical activity (OR 2.04; 95% CI 1.64–2.53) increased the odds of being classified as cognitively impaired (MMSE <25). Similarly, being obese (OR 1.31; 95% CI 1.17–1.47), having hypertension (OR 1.42; 95% CI 1.27–1.57), and having diabetes (OR 1.71; 95% CI 1.40–2.09) increased the odds of cognitive impairment (MoCA <26). High cholesterol was associated with a protective effect (OR 0.79; 95% CI 0.63–0.98) under MMSE <25 classification while, problematic alcohol behavior reduced the odds of being classified as cognitively impaired using MoCA <26 by 35% (OR 0.65; 95% CI 0.55–0.76). Depression was not associated with increased odds of cognitive decline. Lastly, mean quality of life (QoL) scores decreases as severity of cognitive impairment increases from normal to moderate cognitive impairment ( P < 0.001). Conclusions: Several modifiable risk factors for cognitive decline were identified, including smoking, low physical activity, hypertension, diabetes, and obesity. Policies aimed at reducing the prevalence of these risk factors in the population might reduce the impact of cognitive decline on public health.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".