Validation of the hospital frailty risk score in China
Bibliographic record
Abstract
PURPOSE: To validate the Hospital Frailty Risk Score (HFRS) in Chinese hospital settings, describing how patients are allocated to frailty risk groups and how frailty risk is associated with length of stay (LoS) and hospital costs. DESIGN: Retrospective observational study. SETTING: Forty-eight hospitals in Lvliang City, Shanxi Province, China. SUBJECTS: Patients aged 75 years or older hospitalised between 1 January 2022 and 31 December 2023 (n = 34,731). METHODS: A logistic regression model examined the association between long length of stay (LoS) and frailty risk. A generalised linear model assessed the association between hospital costs and frailty risk. Subgroup analyses of age group, sex, and hospital tiers were conducted. RESULTS: 22.2% of patients were categorised as having zero risk, 62.4% as low risk, 15.3% as intermediate risk, and 0.08% as high risk. Compared to the zero risk group: for those with low risk, the probability of long LoS was 1.92 (95% CI 1.79-2.06) times higher and hospital costs were ¥1926 (95% CI 1655-2197) higher; for those with intermediate risk, the probability of long LoS was 2.7 (95% CI 2.49-2.96) times higher and hospital costs were ¥4284 (95% CI 3916-4653) higher; and for those with high risk, the probability of long LoS was 6.7 (95% CI 3.06-14.43) times higher and hospital costs were ¥16,613 (95% CI 12,827-20,399) higher. The explanatory power of the HFRS held across subgroups. CONCLUSIONS: Compared to patients aged 75 + elsewhere, those in China had lower frailty risk scores, likely reflecting a younger age structure and recording of fewer diagnosis codes. Even so, the HFRS is a powerful predictor of long length of stay and hospital costs in China.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".