Health-Related Quality of Life: A Rapid Predictor of Hospitalization in Patients With Cirrhosis
Bibliographic record
Abstract
OBJECTIVES: Patients with cirrhosis experience a worsened quality of life; this may be quantified by the use of health-related QoL (HRQoL) constructs, such as the chronic liver disease questionnaire (CLDQ) and EuroQoL Group-visual analog scale (EQ-VAS). In this multicenter prospective study, we aimed to evaluate HRQoL as a predictor of unplanned hospital admission/early mortality, identify HRQoL domains most affected in cirrhosis, and identify predictors of low HRQoL in patients with cirrhosis. METHODS: Multivariable logistic regression was used to determine independent association of HRQoL with primary outcome and identify predictors of low HRQoL. HRQoL was also compared with population norms. RESULTS: In this cohort of 402 patients with cirrhosis, mean model for end-stage liver disease was 12.5 (4.9). More than 50% of the cohort had low HRQoL, considerably lower than population norms. HRQoL (measured by either CLDQ or EQ-VAS) was independently associated with the primary outcome of short-term unplanned hospitalization/mortality. Every 1-point increase in the CLDQ and every 10-point increase in the EQ-VAS reduced the risk of reaching this outcome by 30% and 13%, respectively. Patients with cirrhosis had lower HRQoL scores than population norms across all domains of the CLDQ. Younger age, female sex, current smoker, lower serum albumin, frailty, and ascites were independently associated with low CLDQ. DISCUSSION: Patients with cirrhosis experience poor HRQoL. HRQoL is independently associated with increased mortality/unplanned hospitalizations in patients with cirrhosis and could be an easy-to-use prognostic screen that patients could complete in the waiting room before their appointment.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".