Liver Bridging Techniques in the Treatment of Acute Liver Failure
Bibliographic record
Abstract
The introduction of orthotopic liver transplantation in the management of acute liver failure has dramatically increased the survival rates of patients at the cost of removing the patient's native liver and life-long dependence on immunosuppression. However, it is well known that in many patients with acute liver failure the diseased liver has the potential to recover. Death in these patients is often due to increased intra-cranial pressure or infection. Liver bridging techniques are assigned to temporarily provide liver function and enable the native liver to recover in patients with acute liver failure. They represent an attractive alternative to conventional liver transplantation in the management of acute liver failure, since after recovery of the native liver the patient is freed from immuno-suppression with all associated side-effects and risks. Auxiliary liver transplantation, artificial liver support devices and hepatocyte transplantation represent different ways of bridging liver function in acute liver failure. The aim of this review is to present the ideas and principles of these three different liver bridging techniques. We will discuss the relative importance and the future potential of theses bridging techniques in the treatment of acute liver failure by comparing the experimental and clinical results.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".