A multicenter study to define sarcopenia in patients with end‐stage liver disease
Bibliographic record
Abstract
Sarcopenia is associated with increased wait‐list mortality, but a standard definition is lacking. In this retrospective study, we sought to determine the optimal definition of sarcopenia in end‐stage liver disease (ESLD) patients awaiting liver transplantation (LT). Included were 396 patients newly listed for LT in 2012 at 5 North American transplant centers. All computed tomography scans were read by 2 individuals with interobserver correlation of 98%. Using image analysis software, the total cross‐sectional area (cm 2 ) of abdominal skeletal muscle at the third lumbar vertebra was measured. The skeletal muscle index (SMI), which normalizes muscle area to patient height, was then calculated. The primary outcome was wait‐list mortality, defined as death on the waiting list or removal from the waiting list for reasons of clinical deterioration. Sex‐specific potential cutoff values to define sarcopenia were determined with a grid search guided by log‐rank test statistics. Optimal search methods identified potential cutoffs to detect survival differences between groups. The overall median SMI was 47.6 cm 2 /m 2 : 50.0 in men and 42.0 in women. At a median of 8.8 months follow‐up, mortality was 25% in men and 36% in women. Patients who died had lower SMI than those who survived (45.6 versus 48.5 cm 2 /m 2 ; P < 0.001), and SMI was associated with wait‐list mortality (hazard ratio, 0.95; P < 0.001). Optimal search method yielded SMI cutoffs of 50 cm 2 /m 2 for men and 39 cm 2 /m 2 for women; these cutoff values best combined statistical significance with a sufficient number of events to detect survival differences between groups. In conclusion, we recommend that an SMI < 50 cm 2 /m 2 for men and < 39 cm 2 /m 2 for women be used to define sarcopenia in patients with ESLD awaiting LT. Liver Transplantation 23 625–633 2017 AASLD .
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".