Liver assistive devices in acute liver failure: Current use and future directions
Bibliographic record
Abstract
Acute liver failure (ALF) is a rare syndrome where rapid deterioration of liver function occurs after an acute insult in a patient without prior chronic liver disease and leads to jaundice, hepatic encephalopathy (HE), and oftentimes multiorgan failure (MOF). At this time, the only definitive treatment for ALF is LT but some patients, particularly APAP-induced ALF patients, may have ongoing regenerative capacity of the liver and may not require LT with ongoing supportive management. As a result, extracorporeal liver support (ECLS) has been a topic of interest both as a bridge to LT and as a bridge to spontaneous recovery and aims to remove damaging toxins that further aggravate liver failure, stimulate regeneration of the liver, and improve pathophysiologic consequences of liver failure. There are currently two categories of ECLS (artificial and bioartificial). Artificial ECLS does not incorporate active hepatocytes and are based on the principles of filtration and adsorption and includes renal replacement therapy (RRT), plasma adsorption including plasma exchange and Prometheus (Fractionated Plasma Separation and Adsorption), and albumin dialysis including MARS (Molecular Adsorbent Recirculating System) and SPAD (Single Pass Albumin Dialysis). Bioartificial ECLS incorporates active hepatocytes (human or porcine in origin) to improve liver detoxification capacity and to support hepatic synthetic function and includes ELAD (Extracorporeal Liver Assist Device) and HepatAssist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".