Validation of the Clinical Frailty Scale for the Prediction of Mortality in Patients With Liver Cirrhosis
Bibliographic record
Abstract
INTRODUCTION: Frailty is a common but often underestimated complication in patients with liver cirrhosis. The Clinical Frailty Scale (CFS) allows the assessment of frailty within a short period of time but has only been investigated in a Canadian cohort of outpatients. The aim of the current study was to evaluate the ability of the CFS to predict mortality in outpatients and nonelectively hospitalized German patients. METHODS: Two hundred outpatients and 99 nonelectively hospitalized patients with liver cirrhosis were prospectively enrolled. Outpatients/inpatients were followed for a median of 364/28 days regarding the primary outcome of death or liver transplantation. Eighty-seven patients of the outpatient cohort and 64 patients of the inpatient cohort had available computed tomography-scans for the quantification of muscle mass. RESULTS: Median CFS was 3 in the outpatient and the inpatient cohort. Twenty-one (10.5%) outpatients were at least prefrail (CFS > 3) and 26 (26.3%) inpatients were frail (CFS > 4). For every one-unit increase, there was an independent association between the CFS and mortality in the outpatient cohort (hazard ratio 1.534, P = 0.007). This association remained significant after controlling for muscle mass in the subcohort with available computed tomography scans. In the inpatient cohort, frailty (CFS > 4) was an independent predictor for 28-day mortality after controlling for acute-on-chronic liver failure, albumin, and infections (odds ratio 4.627, P = 0.045). However, this association did not reach significance in a subcohort after controlling for muscle mass. DISCUSSION: Especially in outpatients, CFS is a useful predictor regarding increased mortality independent of the muscle mass.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".