Quality of Life in Elders with Suspected Alzheimer Disease: An Urban Health Centers-Based Study from Iran
Bibliographic record
Abstract
<b><i>Background/Aims:</i></b> Quality of life (QOL) and Alzheimer disease (AD) among older people have been recognized as public health challenges. Here, we investigated the association between QOL and AD in the elders. <b><i>Methods:</i></b> In this cross-sectional study, elderly people were selected from urban health centers (Shiraz, Iran) by multistage cluster random sampling and were interviewed using LEIPAD (for QOL) and Montreal Cognitive Assessment (for AD) questionnaires. The data was analyzed using Mplus (version 6.12) and IBM SPSS (version 25) software. <b><i>Results:</i></b> The participants consisted of 182 elderly with a mean age of 67 ± 5.05 years, and 95 (52.2%) of them were females. There were 161 (88.5%) and 130 (71.4%) cases educated up to 12 years and married, respectively. Furthermore, 46 (25.3%) had low-to-moderate QOL, and 132 (72.5%) were suspected to have AD. QOL was inversely associated with AD, and men (β = –0.310) were more affected than women (β = –0.290). AD (β = –0.298), age (β = –0.288), hypertension (β = –0.267), education (β = 0.260), and body mass index (β = –0.198) were determinants of QOL. Also, physical activity was indirectly associated with QOL (β = 0.076). AD was correlated with the cognitive functioning component of QOL (<i>r</i> = –0.72). <b><i>Conclusion:</i></b> One elder out of 4, did not have desirable QOL and 3 elders out of 4 were suspected to have AD. AD can decrease QOL among the older people. Screening of the elders for AD is recommended to improve their QOL by health centers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".