Sarcopenia HIBA score predicts sarcopenia and mortality in patients on the liver transplant waiting list
Bibliographic record
Abstract
Sarcopenia is a prevalent condition that predicts prognosis in patients awaiting liver transplantation (LT). The gold standard for the diagnosis of sarcopenia is the assessment of the muscular area at L3 with computed tomography (CT) scan (skeletal muscle index [SMI]), but the routine use of CT scan is limited in clinical practice. Thus, we designed a single-center observational study aimed to evaluate the clinical factors associated with the presence of sarcopenia by SMI, and to build a score capable of predicting or excluding the presence of sarcopenia in patients on the LT waiting list (WL). Binary logistic regression analysis was performed to establish the factors independently associated with sarcopenia, and the Sarcopenia Hospital Italiano de Buenos Aires (HIBA) score was built from the resulting model after internal validation analysis by bootstrapping and correction for optimism. The predictive capability of mortality on the WL was evaluated with competing risk regression analysis. A total of 215 patients with cirrhosis on the LT WL were included. The independent factors associated with the presence of sarcopenia were male sex (odds ratio [OR]: 6.09, p < 0.001), body mass index (OR: 0.74, p < 0.001), Child Pugh (OR: 1.44, p < 0.001), and the ratio creatinine/Cystatin C (OR: 0.03, p = 0.007). The Sarcopenia HIBA score constructed with these variables showed an area under the curve of 0.862. During follow-up, 77 (36%) patients underwent LT, 46 (21%) died, and 92 (43%) remained alive. After adjusting for Model for End-Stage Liver Disease-Sodium, Sarcopenia HIBA score was an independent predictor of WL mortality (subhazard ratio: 1.19; 95% confidence interval 1.01-1.40; p = 0.042). Sarcopenia HIBA score is an easy-to-use, objective, and reliable diagnostic and predictive tool that can be useful to improve the prognostic evaluation and allow identifying a group of patients with a higher risk of death while awaiting LT.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".