A Karnofsky performance status–based score predicts death after hospital discharge in patients with cirrhosis
Bibliographic record
Abstract
Identification of patients with cirrhosis at risk for death within 3 months of discharge from the hospital is essential to individualize postdischarge plans. The objective of the study was to identify an easy-to-use prognostic model based on the Karnofsky Performance Status (KPS). The North American Consortium for the Study of End-Stage Liver Disease consists of 16 tertiary-care hepatology centers that prospectively enroll nonelectively admitted cirrhosis patients. Patients enrolled had KPS assessed 1 week postdischarge. KPS was categorized into low (score 10-40), intermediate (50-70), and high (80-100). Of 954 middle-aged patients (57 ± 10 years, 63% men) with a median Model for End-Stage Liver Disease (MELD) score of 17 (interquartile range 13-21), the mortality rates for the low, intermediate, and high performance status groups were 23% (36/159), 11% (55/489), and 5% (15/306), respectively. Low, intermediate, and high performance status was seen in 17%, 51%, and 32% of the cohort, respectively. Low performance status was associated with older age, dialysis, hepatic encephalopathy, longer length of stay, and higher white blood cell count or MELD score at discharge. A model was derived using the three independent predictors of 3-month mortality: KPS, age, and MELD score. This score had better discrimination (area under the receiver operating characteristic curve = 0.74) than a model using MELD (area under the receiver operating characteristic curve = 0.62) or MELD and age (area under the receiver operating characteristic curve = 0.67) to predict 3-month mortality. CONCLUSIONS: Cirrhosis patients at risk for 3-month postdischarge mortality can be identified using a novel KPS-based score; this score may be adopted in practice to guide postdischarge early interventions, including the integrated provision of active and palliative management strategies. (Hepatology 2017;65:217-224).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".