Review article: the design of clinical trials in hepatic encephalopathy - an International Society for Hepatic Encephalopathy and Nitrogen Metabolism (ISHEN) consensus statement
Bibliographic record
Abstract
BACKGROUND: The clinical classification of hepatic encephalopathy is largely subjective, which has led to difficulties in designing trials in this field. AIMS: To review the current classification of hepatic encephalopathy and to develop consensus guidelines on the design and conduct of future clinical trials. METHODS: A round table was convened at the 14th International Society for Hepatic Encephalopathy and Nitrogen Metabolism (ISHEN) meeting. Key discussion points were the nomenclature of hepatic encephalopathy and the selection of patients, standards of care and end-points for assessing the treatment and secondary prevention of hepatic encephalopathy. RESULTS: It was generally agreed that severity assessment of hepatic encephalopathy in patients with cirrhosis, whether made clinically or more objectively, should be continuous rather than categorical, and a system for assessing the SONIC (Spectrum of Neuro-cognitive Impairment in Cirrhosis) was proposed. Within this system, patients currently classified as having minimal hepatic encephalopathy and Grade I hepatic encephalopathy would be classified as having Covert hepatic encephalopathy, whereas those with apparent clinical abnormalities would continue to be classified as overt hepatic encephalopathy. Some aspects of the terminology require further debate. Consensus was also reached on the patient populations, standards of care and endpoints to assess clinical trial outcomes. However, some compromises had to be made as there is considerable inter- and intravariability in the availability of some of the more objective surrogate performance markers. CONCLUSIONS: The objectives of the round table were met. Robust, defendable guidelines for the conduct of future studies into hepatic encephalopathy have been provided. Outstanding issues are few and will continue to be discussed.
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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 | 0.092 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".