Bibliographic record
Abstract
Bioartificial liver support has been increasingly the focus of both basic and clinical research in an attempt to replicate the multiplicity of normal liver function. The concept is attractive because, if it is effective, patients with acute liver failure may be supported until native liver regeneration occurs or, by optimizing their condition, until liver transplantation is possible. Current bioartificial liver support systems utilize primary porcine hepatocytes or transformed human hepatocytes, which are housed within a bioreactor, through which the patient's blood or plasma is pumped in an extracorporeal circuit. The optimal source for the hepatocytes is an area of debate; however, a genetically engineered cell line may provide optimal function. Novel three-dimensional matrices that anchor the hepatocytes are being designed to mimic architectural features of the normal liver. Large multicentre, randomized, controlled trials are ongoing following several pilot studies. Serious side effects such as hemodynamic instability and immune reactions have been infrequent. Much controversy, however, surrounds the issue of possible transmission of pig endogenous retrovirus to humans, and current trials are being carefully monitored. Bioartificial liver support is a promising technology, and the results of current and planned studies are awaited with great interest.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".