A Rapid Bedside Screen to Predict Unplanned Hospitalization and Death in Outpatients With Cirrhosis: A Prospective Evaluation of the Clinical Frailty Scale
Bibliographic record
Abstract
OBJECTIVES: Screening tools to determine which outpatients with cirrhosis are at highest risk for unplanned hospitalization are lacking. Frailty is a novel prognostic factor but conventional screening for frailty is time consuming. We evaluated the ability of a 1 min bedside screen (Clinical Frailty Scale (CFS)) to predict unplanned hospitalization or death in outpatients with cirrhosis and compared the CFS with two conventional frailty measures (Fried Frailty Criteria (FFC) and Short Physical Performance Battery (SPPB)). METHODS: We prospectively enrolled consecutive outpatients from three tertiary care liver clinics. Frailty was defined by CFS >4. The primary outcome was the composite of unplanned hospitalization or death within 6 months of study entry. RESULTS: A total of 300 outpatients were enrolled (mean age 57 years, 35% female, 81% white, 66% hepatitis C or alcohol-related liver disease, mean Model for End-Stage Liver Disease (MELD) score 12, 28% with ascites). Overall, 54 (18%) outpatients were frail and 91 (30%) patients had an unplanned hospitalization or death within 6 months. CFS >4 was independently associated with increased rates of unplanned hospitalization or death (57% frail vs. 24% not frail, adjusted odds ratio 3.6; 95% confidence interval (CI): 1.7-7.5; P=0.0008) and there was a dose response (adjusted odds ratio 1.9 per 1-unit increase in CFS, 95% CI: 1.4-2.6; P<0.0001). Models including MELD, ascites, and CFS >4 had a greater discrimination (c-statistic=0.84) than models using FFC or SPPB. CONCLUSIONS: Frailty is strongly and independently associated with an increased risk of unplanned hospitalization or death in outpatients with cirrhosis. The CFS is a rapid screen that could be easily adopted in liver clinics to identify those at highest risk of adverse events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".