Evaluation of clinical frailty screening in geriatric acute care
Bibliographic record
Abstract
BACKGROUND: While frailty status is an attractive risk stratification tool, the evaluation of frailty in acute care can be challenging as some inpatients are unable to complete performance-based tests as part of frailty assessment and some tools may lack discriminative ability and categorize majority of cohorts as "frail". In this study, we evaluated the feasibility of frailty screening with the simple clinical frailty scale (CFS) by different clinicians, and its association with mortality and rehospitalization in a geriatric acute care setting. METHODS: This study took place in Geriatric Medicine Department of a General Hospital in Singapore. We analysed records of 314 inpatients aged 70 years and older. At baseline, premorbid frailty was assessed using the CFS of the Canadian Study on Health and Aging. Demographic characteristics and other variables were retrieved from their medical records. Primary outcomes were mortality and rehospitalization during the 6-month follow-up. Survival analysis was used to compare the time to death and rehospitalization among CFS categories (1-4: nonfrail, 5-6: mild-moderate frail, and 7-8: severe frail). RESULTS: CFS showed a high inter-rater reliability when used by different clinicians. In the Cox proportional hazard model controlling for age, gender, Charlson comorbidity index, modified severity of illness index, and discharge placements, severe frailty determined by CFS (HR = 2.09, 95% CI = 1.01-4.33, P = 0.047) and CFS scores (HR = 1.27, 95% CI = 1.05-1.53, P = 0.012) were significantly associated with higher mortality until 6-month postdischarge, but not rehospitalization. CONCLUSION: Frailty status determined by CFS adds to disease severity and comorbidity in predicting short-term mortality but not rehospitalization in older inpatients who received geriatric acute care in our setting. CFS is reliable and has the potential to be incorporated into routine screening to better identify, communicate, and address frailty in the acute settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.109 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".