Application of CSHA Frailty Index and Clinical Frailty Scale in Geriatric Assessment of Elderly Males in China
Bibliographic record
Abstract
People at an advanced age often live with multiple chronic diseases as well as disabilities. To access the health condition of elderly persons, various models have been used in developed countries in the West. However, it is not known how these models apply to the elderly in Asian developing countries. This study investigated the application of the Clinical Frailty Scale-09, a widely used method in patient assessment in Western countries, to older adults living in mainland China. Two hundred and ten elderly males were assessed for their health conditions using a list of variables by the Canadian Study of Health and Aging to construct the 70-item CSHA Frailty Index. The obtained Frailty Indices were compared with the scores of The Clinical Frailty Scale-09 for the same sample group. The assessment revealed the changing pattern in the health condition of the sampled population. Compared to the group aged 65-74 years old, the Frailty Index and the Clinical Frailty Scale increased in the groups aged 75-84 and 85-89 years old. The greatest increase was in the group aged ≥90 years old. The scores of the Frailty Index and the Clinical Frailty Scale-09 correlate with each other. These findings suggest that the Frailty Index and Clinical Frailty Scale-09 provide reliable assessment of the health condition of elderly Chinese males.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".