AB110. SOH22ABS195. Sarcopenia is an important predictor of frailty in patients awaiting liver transplant
Bibliographic record
Abstract
Background: Frail patients are at increased risk of mortality and hospitalisation prior to transplant. Over the past 20 years, obesity rates have greatly increased, and there are larger proportion of obese patients undergoing transplantation. The aim of this study was to identify if body composition impacts frailty in patients awaiting liver transplant. Methods: Patients were recruited and prospectively evaluated while undergoing liver transplant assessment. Clinical frailty assessments included Liver Frailty Index (LFI), Fried Frailty Index (FFI) and Rockwood Frailty Score (RFS). Body composition was assessed from CT images using Slice-O-Matic 5 software (TomoVision, Canada). The programme then calculated adipose tissue, skeletal muscle area and the skeletal muscle index (SMI) (total abdominal skeletal muscle area cm2/height). Sarcopenia has been defined as an SMI less than 50 cm/m2 for men and 39 cm/m2 for women. Results: A total of 55 patients were assessed for transplant and had a suitable CT carried out between the collection period. Forty-two percent [38] were sarcopenic. SMI did not correlate with clinical frailty scores (FFI r=−0.088, P=0.522, RFS r=−0.037, P=0.785). Increased visceral adiposity had the highest associated with frailty, significantly correlating with increased LFI (r=0.334, P=0.003), FFI (r=0.287, P=0.011), RFS (r=0.297, P=0.008), TUG (r=0.354, P=0.002). Hepatic encephalopathy [odds ratio (OR) 211.683, 95% confidence interval (CI): 3.069–44.473, P<0.001] and visceral adipose tissue (OR 1.009, 95% CI: 1.001–1.017, P=0.031) significantly increased the odds of frailty using the LFI. Conclusions: Increased volume of adipose tissue was significantly associated with multiple clinical frailty assessments. This study adds to our understanding of factors affecting the development of frailty in these cohort of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".