[Current status and influencing factors regarding quality of life among patients with Alzheimer's disease].
Bibliographic record
Abstract
OBJECTIVE: To investigate the current status and influencing factors regarding quality of life among patients with Alzheimer's disease (AD). METHODS: Through stratified cluster sampling method, two hundred patients with AD and their caregivers were chosen and interviewed. AD patients were assessed by questionnaires, Montreal Cognitive Assessment, and Quality of Life-Alzheimer's Disease (QOL-AD), in order to compare the reports from patients and caregivers on QOL-AD and to analyze related influencing factors. Descriptive analysis, paired t-test, analysis of variance (ANOVA), Pearson's correlation and multiple linear regression were performed using the Statistical Package for Social Science (SPSS). RESULTS: The scores of reports from patients (28.78 ± 4.30) were lower than that from the caregivers' (30.05 ± 6.05). The difference was statistically significant (t = 2.122, P < 0.05) and was positively correlated (r = 0.312, P < 0.001). Data from multivariate analysis showed that cognitive level (t = 3.465, P = 0.001), marriage relationship (t = 3.062, P = 0.003), having public activities (t = 2.581, P = 0.011), personal characters (t = 2.254, P = 0.026), restricted diet pattern (t = 3.614, P < 0.001), regularly drinking tea (t = 2.652, P = 0.009) and doing housework (t = 3.180, P = 0.002) were predictive factors. CONCLUSION: Scores from the caregiver's report on QOL-AD were higher than that from the patients'. Many factors influenced the quality of life on AD patients. Strategies on improving the quality of life among AD patients can be developed based on the findings of this study.
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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".