Study protocol to redefine muscle attenuation cut-offs for better prediction of mortality in patients with cirrhosis: a comprehensive post hoc validation study – a study protocol
Bibliographic record
Abstract
INTRODUCTION: Myosteatosis, characterised by altered muscle composition detectable by muscle radiodensity attenuation on CT scans, has been associated with increased mortality in patients with cirrhosis. However, standard attenuation cut-offs, derived primarily from oncology populations, may not be appropriate for patients with cirrhosis. This study protocol aims to address this diagnostic gap by validating the Ebadi cut-offs, which are based on a retrospective cohort and have not been extensively validated in a cirrhotic population. The aim of the study is to refine these cut-offs for more accurate prediction of mortality in patients with cirrhosis using two independent patient cohorts (retrospective and prospective). METHODS AND ANALYSIS: This post hoc validation study analyses muscle weakness cut-offs in patients with cirrhosis using data from two independent cohorts. A total of 1537 patients will be analysed. The study will assess interobserver variability to ensure robust results by analysing random samples of 60 patients from the two cohorts. Statistical methods will be used to determine the accuracy and relevance of current cut-offs in predicting patient mortality. The analysis will also examine the relationship between muscle wasting and clinical outcomes in cirrhosis and the relationship with muscle mass loss. ETHICS AND DISSEMINATION: Ethical approval for this study has been obtained from the relevant institutional review boards. The results will be disseminated through presentations at scientific conferences and publication in peer-reviewed journals. The results of the study are expected to contribute to improved diagnostic criteria for myosteatosis in cirrhosis, providing clinicians with more tailored and accurate tools for cirrhosis prognosis. TRIAL REGISTRATION NUMBER: NCT06593015.
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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 | 0.041 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".