Plenary and Parallel Sessions (Abstracts 1–258)
Bibliographic record
Abstract
INTRODUCTION Sarcopenia is a life-threatening complication of cirrhosis, associated with higher waitlist mortality (WLM) .Despite an association between muscle mass and outcomes in liver transplant (LT) candidates, no definition of sarcopenia in this population exists .We sought to determine the optimal definition of sarcopenia in ESLD patients awaiting LT .METHODS Subjects Multi-center study from 5 North American centers .Included were all adult patients newly listed for liver transplant from 1/1/12-12/31/12 with an abdominal CT scan within 3 months of listing .Measurements of muscle mass CT scans were read by 2 individuals with interobserver agreement of 98% .Total cross-sectional area (cm 2 ) of abdominal skeletal muscles at L3 was obtained, including psoas, paraspinal, and abdominal wall muscles .Using image analysis software, the cross-sectional area of these muscles was semi-automatically measured .Skeletal muscle index (SMI) calculated as: SMI (cm 2 /m 2 ) = (total abdominal skeletal muscle area in cm 2 ) / (height in meters) 2 Statistical analysis The primary outcome was WLM, defined as death prior to LT or delisting for clinical deterioration .Patients were censored at the time of LT or removal from the waitlist for nonclinical reasons .Associations between SMI and mortality were assessed using competing risks regression .Significant variables in univariable analysis were entered into a stepwise backwards regression model .Optimal stratification with Cox regression was used to identify potential cut-offs to define sarcopenia .RESULTS In 396 patients: median age 58, 70% male, and median MELD 15 .Majority were Caucasian (71%), with racial diversity: 11% Hispanic, 8% Asian and 5% Black .39% had HCC .Overall median SMI was 47 .6cm 2 /m 2 (IQR 41 .8-53.6):50 .0(44 .2-55.2) in men and 42 .0(36 .1-46.7) in women .At a median of 8 .8months (3-21 .7) of follow-up, 25% of men and 36% of women had WLM .Those with WLM had significantly lower SMI (45 .6 vs 48 .5, p<0 .001) .In univariable analysis, SMI was strongly associated with WLM (HR 0 .95,p<0 .001),remaining significant (0 .95, p<0 .0001)after adjustment for black race and HCC .Optimal stratification yielded an SMI cut-off of 49 (men) and 39 (women) .Of 277 men, 45% had SMI < 49 with 78% increased risk of WLM (logrank p=0 .009) .Of 119 women, 33% had SMI < 39 with 343% increased risk of WLM (logrank p<0 .001) .CONCLUSIONS Our multicenter study is the first to provide an evidence-based definition of sarcopenia in ESLD .We propose that SMI of <49 cm 2 /m 2 in men and <39 cm 2 /m 2 in women should define sarcopenia in patients with ESLD awaiting LT .A standardized definition is essential to advance understanding of this devastating complication .
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.517 | 0.310 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".