An Analysis of the Effect of Stroke on Health-Related Quality of Life of Older Adults With Coronary Heart Disease Who Take Aspirin
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the impact of coronary heart disease (CHD) on health-related quality of life (HRQoL) among individuals taking aspirin, as well as to explore the potential association between stroke and CHD on HRQoL. METHOD: A total of 17,106 respondents aged 50 years and above who reported using aspirin on "some days" or "daily" were included in the analysis. Among them, 4,036 individuals had a history of coronary heart disease. We utilized the Chi-square test to assess the proportion of individuals with CHD who reported poor self-rated health and experienced poor HRQoL in four domains: physical health, mental health, physical and mental health combined, and the number of days limited by poor health. Logistic regression was employed to investigate the interaction between stroke and CHD concerning the quality of life. RESULT: Among adults aged 50 years and above using aspirin, those with CHD tended to be older (68.7 years ± 0.37 vs 66.6 ± 0.24), had a higher proportion of male respondents (60.0% vs 45.1%), and were mostly of white ethnicity (77.4% vs 76.2%). The group with CHD reported significantly poorer self-rated health compared to those without CHD (52.1% vs 25.6%, p<0.001), along with a higher prevalence of poor physical health (55.3% vs 42.7%, p<0.001) and poor mental health (50.2% vs 40.4%, p = 0.033) in comparison to aspirin users without CHD. However, there was no statistically significant association between stroke and CHD concerning the impact on all domains of quality of life (p>0.05). CONCLUSION: Our findings indicate that individuals aged 50 years and above with CHD who are using aspirin experience a lower quality of life in both the physical and mental health domains when compared to their counterparts without CHD. Furthermore, there was no significant interaction between stroke and CHD in relation to the impact on HRQoL in 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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) | 0.002 | 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".