Malnutrition Impacts Health‐Related Quality of Life in Cirrhosis: A Cross‐Sectional Study
Bibliographic record
Abstract
Abstract Background To explore the influence of nourishment state measured by various nutrition assessment tools (NATs) on health‐related quality of life (HRQoL) in a pretransplant population with cirrhosis. Methods We collected demographic, nutrition assessment, and disease specific data on 81 patients. HRQoL was measured with the Short‐Form 36 and divided into 8 subscales. Significant relationships between NATs and HRQoL were examined using independent sample t ‐tests, χ 2 analyses, correlations, and multiple and logistic regression adjusted for age and gender. Results Study mean age was 54.2 years (SD 10.4 years), and 57% were male. Subjective Global Assessment (SGA) was significantly related to all HRQoL subscales, except bodily pain and mental health. In the adjusted regression models, general health, vitality, and social functioning were all significantly lower in patients with poorer nutrition status measured using SGA (adjusted R 2 = 11%, β = −0.34, p < 0.01; adjusted R 2 = 8%, β = −0.27, P < 0.05; and adjusted R 2 = 12%, β = −0.38, P < 0.01, Q4 respectively). Physical functioning improved as hand grip strength increased (adjusted R 2 = 20%, β = 0.36, P < 0.01). MELDNa demonstrated a significant negative relationship with role‐emotional (adjusted R 2 = 3%, β = 0.25, P < 0.05), and mid‐arm circumference did not demonstrate any significant relationships with HRQoL. Conclusions Malnutrition assessed by SGA is associated with lower HRQoL in patients with cirrhosis. Future research should identify if nutrition interventions can effectively improve HRQoL in cirrhosis patients.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".