Frailty Predicting Health-Related Quality of Life Trajectories in Individuals with Sarcopenia in Liver Cirrhosis: Finding from BCAAS Study
Bibliographic record
Abstract
The association between frailty and health-related quality of life (HRQoL) among Asian patients with liver cirrhosis and sarcopenia remains largely unexplored. To address this knowledge gap, we conducted a cross-sectional study involving individuals aged 32 to 69 years, all diagnosed with liver cirrhosis. The chronic liver disease questionnaire (CLDQ) was used to assess HR-QoL, the CLDQ score was used as an outcome to measure the factors related to HR-QoL, and the liver frailty index (LFI) was used to assess the frailty status. The association between the frailty status and the CLDQ summary scales was investigated using the correlation coefficient and multiple regression analyses. A total of 138 patients in the frail (n = 62) and non-frail (n = 76) groups with (alcohol: 97; viral: 24; autoimmune: 17; and cryptogenic: 12) were included in the study. Age, CTP score, and model for end-stage liver disease (MELD) sodium were significantly higher in the frail group. In the CLDQ domains, there was a significant difference between the frail and non-frail groups (p value = 0.001). In health-related quality-of-life summary measures, there was a strong negative correlation between frailty and the scores for activities, emotional function, and fatigue (p value = 0.001). When comparing frail to non-frail patients, these characteristics demonstrated significantly increased odds as indicated by their adjusted odds ratios: OR 3.339 (p value = 0.013), OR 3.998 (p value = 0.006), and OR 4.626 (p value = 0.002), respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".