Prevalence, overlap, and interrelationships of physical, cognitive, psychological, and social frailty among community-dwelling older people in Japan
Bibliographic record
Abstract
Background The aim of this study is to explore the prevalence and overlap of physical, cognitive, psychological, and social frailty and their negative-interrelationships. Methods We conducted a survey of people aged ≥75 years in the region with the oldest population in Japan. Frailty was divided into physical, cognitive, psychological, and social frailty, which were evaluated with the Japanese version of the Cardiovascular Health Study (J-CHS) criteria, J-CHS and the Japanese version of the Montreal Cognitive Assessment, the Geriatric Depression Scale-15 and the Lubben Social Network Scale, respectively. Results Of the 268 participants (aged 81.5 ± 4.5 years), 48.1% and 8.6% had physical prefrailty and frailty; 68.3%, 13.0%, and 5.2% had mild cognitive impairment, dementia, and cognitive frailty; 25.7% and 5.2% had depressive mood and depression as psychological frailty; and 7.8% had social frailty, respectively. Path analysis showed that social frailty was associated with psychological frailty. Psychological frailty was associated with physical frailty. Physical frailty was associated with cognitive frailty. Multiple logistic regression analysis showed that independent determinants of physical robustness were female sex, age, and psychological robustness (odds ratio (OR) = 2.166, 0.831, 3.625, respectively). Determinants of cognitive robustness were age and psychological robustness (OR = 0.837, 7.079). Determinants of psychological robustness were physical, cognitive, and social robustness (OR = 3.759, 6.829, 5.037), and the determinant of social robustness was psychological robustness (OR = 4.489), respectively. Conclusions We demonstrated the prevalence, overlap, and interrelationships of different types of frailty and clarified the factors that may help to reduce frailty.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".