Radiological assessment of skeletal muscle index and myosteatosis and their impact postoperative outcomes after liver transplantation
Bibliographic record
Abstract
BACKGROUND: Liver transplantation offers curative treatment to patients with acute and chronic end-stage liver disease. The impact of nutritional status on postoperative outcomes after liver transplantation remains poorly understood. The present study investigated the predictive value of radiologically assessed skeletal muscle index (SMI) and myosteatosis (MI) on postoperative outcomes. PATIENTS AND METHODS: Data of 138 adult patients who underwent their first orthotopic liver transplantation were retrospectively analysed. SMI and MI in computer tomography (CT) scan at the third lumbar vertebra level were calculated. Results were analyzed for the length of hospitalisation and postoperative outcomes. RESULTS: In 63% of male and 28.9% of female recipients, low SMI was found. High MI was found in 45(32.6%) patients. Male patients with high SMI had longer intensive care unit (ICU) stay (P < 0.025). Low SMI had no influence on ICU stay in female patients (P = 0.544), length of hospitalisation (male, P > 0.05; female, P = 0.843), postoperative complication rates (males, P = 0.883; females, P = 0.113), infection rate (males, P = 0.293, females, P = 0.285) and graft rejection (males, P = 0.875; females, P = 0.135). The presence of MI did not influence ICU stay (P = 0.161), hospitalization (P = 0.771), postoperative complication rates (P = 0.467), infection rate (P = 0.173) or graft rejection rate (P = 0.173). CONCLUSIONS: In our study, changes in body composition of liver transplant recipients observed with SMI and MI had no impact on postoperative course after liver transplantation. CT body composition analysis of recipients and uniformly accepted cut-off points are crucial to producing reliable data in the future.
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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".